Escalation of feedback instances

ABSTRACT

Concepts and technologies disclosed herein are directed to escalation of feedback instances. In one aspect disclosed herein, a feedback instance escalation resolver (“FIER”) system can receive a feedback instance escalation request from an original feedback instance. The feedback instance escalation request can identify a deficiency in the original feedback instance. The FIER system can examine the feedback instance escalation request and an objective of the original feedback instance to identify a new objective of an escalated feedback instance. The FIER system can create a definition for the escalated feedback instance to satisfy the objective and can map the definition for the escalated feedback instance to an existing feedback instance model. The FIER system can generate a feedback instance escalation realization request directed to a feedback instance orchestrator and controller (“FIOC”) system. The feedback instance escalation realization request can instruct the FIOC system to realize the escalated feedback instance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 14/675,678, entitled “Escalation of FeedbackInstances,” filed Mar. 31, 2015, now U.S. Pat. No. 10,277,666, which isincorporated herein by reference in its entirety.

BACKGROUND

User-defined network cloud (“UDNC”) strategic objectives includeexploiting the economic advantages of running network functions ongeneral purpose hardware platforms using cloud technology to manageresources elastically based upon business and technical policies.Services can be designed, created, deployed, and managed in near-realtime, rather than requiring software development cycles to create ormodify services. Enhanced control, orchestration, management, and policy(“ECOMP”) is the framework that provides service creation andoperational management of UDNC. ECOMP enables significant reductions inthe time and cost required to develop, deploy, operate, and retireproducts, services, and networks.

Within ECOMP frameworks, policy has emerged as the brain trust thatenables dynamic real-time decision making processes. Policy plays a keyrole in the “feedback instance” domain. A feedback instance typicallyinvolves at least two actors, including policy as one of the actors, butin many cases, a feedback instance involves more than two actors. Othercommon actors might include orchestrators, controllers, networkfunctions, analytic modules, combinations thereof, and the like. In thecurrent market, feedback instances are constructed statically toidentify and/or to solve certain known anomalies. This approach will notscale up in the highly virtualized, real-time, and dynamic environmentof UDNC.

SUMMARY

Concepts and technologies disclosed herein are directed to theescalation of feedback instances. According to one aspect of theconcepts and technologies disclosed herein, a feedback instanceescalation resolver (“FIER”) system can receive a feedback instanceescalation request from an original feedback instance. The feedbackinstance escalation request can identify a deficiency in the originalfeedback instance. The FIER system can examine the feedback instanceescalation request and an objective of the original feedback instance toidentify a new objective of an escalated feedback instance. The FIERsystem can create a definition for the escalated feedback instance tosatisfy the objective and can map the definition for the escalatedfeedback instance to an existing feedback instance model. The FIERsystem can generate a feedback instance escalation realization requestdirected to a feedback instance orchestrator and controller (“FIOC”)system. The feedback instance escalation realization request caninstruct the FIOC system to realize the escalated feedback instance.

In some embodiments, the definition for the escalated feedback instanceincludes a target and an input to satisfy, at least in part, the newobjective. The FIER system, in some embodiments, can map the definitionfor the escalated feedback instance to an active feedback instancemodel. The FIER system can map the definition to the active feedbackinstance model utilizing a table of all or a relevant subset ofavailable feedback instance models. The FIER system can map thedefinition to the active feedback instance model and to a further activefeedback instance model. The FIER system can calculate a distance scorefor the active feedback instance model from the definition for theescalated feedback instance and a further distance score for the furtheractive feedback instance model from the definition for the escalatedfeedback instance. The FIER system can select either the distance scoreor the further distance score based upon a closest match to thedefinition for the escalated feedback instance.

The FIER system, in some embodiments, can map the definition for theescalated feedback instance to a non-active feedback instance model. TheFIER system can map the definition to the non-active feedback instancemodel utilizing a table of all or a relevant subset of availablefeedback instance models. The FIER system can map the definition to thenon-active feedback instance model and to a further non-active feedbackinstance model. The FIER system can calculate a distance score for thenon-active feedback instance model from the definition for the escalatedfeedback instance and a further distance score for the furthernon-active feedback instance model from the definition for the escalatedfeedback instance. The FIER system can select either the distance scoreor the further distance score based upon a closest match to thedefinition for the escalated feedback instance.

According to another aspect of the concepts and technologies disclosedherein, a computer-readable storage medium can includecomputer-executable instructions. The computer-executable instructionscan be executed by a processing unit of a FIER system to cause the FIERsystem to perform operations. The FIER system can perform operations thesame as or similar to those described above.

According to another aspect of the concepts and technologies disclosedherein, a FIOC system can receive a feedback instance escalationrealization request to realize an escalated feedback instance based uponan existing feedback instance model to satisfy an objective and to curea deficiency in an original feedback instance. The FIOC system candetermine whether to activate a non-active feedback instance model asthe existing feedback instance model upon which to realize the escalatedfeedback instance or to associate the feedback instance escalationrealization request with an active feedback instance based upon anactive feedback instance model as the existing feedback instance modelupon which to realize the escalated feedback instance. If adetermination to activate the non-active feedback instance model as theexisting feedback instance model is made, the FIOC system can retrievethe non-active feedback instance model and can activate the escalatedfeedback instance based upon the non-active feedback instance model.

In some embodiments, if a determination to activate the active feedbackinstance model as the existing feedback instance model is made, the FIOCsystem can associate the feedback instance escalation realizationrequest with the active feedback instance model to realize the activefeedback instance as the escalated feedback instance.

In some embodiments, the FIOC system can determine to re-activate thenon-active feedback instance model as the existing feedback instancemodel upon which to realize the escalated feedback instance. In theseembodiments, the non-active feedback instance model was previouslyactivated to realize one or more feedback instances.

In some embodiments, the deficiency in the original feedback instanceincludes a number of actors. In some embodiments, the deficiency in theoriginal feedback instance includes a type of actor. In someembodiments, the deficiency in the original feedback instance includesan inactivity of a particular actor. In some embodiments, the deficiencyin the original feedback instance includes a number of operationalparameters. In some embodiments, the deficiency in the original feedbackinstance includes a type of operational parameter. In some embodiments,the deficiency in the original feedback instance includes an analyticapplication. In some embodiments, the deficiency in the originalfeedback instance includes another attribute.

It should be appreciated that the above-described subject matter may beimplemented as a computer-controlled apparatus, a computer process, acomputing system, or as an article of manufacture such as acomputer-readable storage medium. These and various other features willbe apparent from a reading of the following Detailed Description and areview of the associated drawings.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intendedthat this Summary be used to limit the scope of the claimed subjectmatter. Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating aspects of a feedback instancearchitecture, according to an illustrative embodiment.

FIGS. 2A-2B is a flow diagram illustrating aspects of a method foroperating the feedback instance architecture, according to anillustrative embodiment.

FIG. 3 is a block diagram illustrating further aspects of the feedbackinstance architecture, according to an illustrative embodiment.

FIG. 4 is a flow diagram illustrating aspects of a method for creatingand managing feedback instances in the feedback instance architecture,according to an illustrative embodiment of the concepts and technologiesdisclosed herein.

FIG. 5 is a block diagram illustrating further aspects of the feedbackinstance architecture, according to an illustrative embodiment.

FIG. 6 is a flow diagram illustrating aspects of a method for modifyingan existing feedback instance in the feedback instance architecture,according to an illustrative embodiment of the concepts and technologiesdisclosed herein.

FIG. 7 is a block diagram illustrating further aspects of the feedbackinstance architecture, according to an illustrative embodiment.

FIG. 8 is a flow diagram illustrating aspects of a method for escalatinga feedback instance in the feedback instance architecture, according toan illustrative embodiment of the concepts and technologies disclosedherein.

FIG. 9 is a block diagram illustrating further aspects of the feedbackinstance architecture, according to an illustrative embodiment.

FIG. 10 is a flow diagram illustrating aspects of a method forconsulting among feedback instances in the feedback instancearchitecture, according to an illustrative embodiment of the conceptsand technologies disclosed herein.

FIG. 11 is a block diagram illustrating further aspects of the feedbackinstance architecture, according to an illustrative embodiment.

FIG. 12 is a flow diagram illustrating aspects of a method forcoordinating multiple feedback instances to determine optimal actions inthe feedback instance architecture, according to an illustrativeembodiment of the concepts and technologies disclosed herein.

FIG. 13 is a block diagram illustrating further aspects of the feedbackinstance architecture, according to an illustrative embodiment.

FIG. 14 is a flow diagram illustrating aspects of a method for selectingfeedback instance modes, according to an illustrative embodiment.

FIG. 15 is a block diagram illustrating an example computer systemcapable of implementing aspects of the embodiments presented herein.

FIG. 16 is a diagram illustrating a network, according to anillustrative embodiment.

DETAILED DESCRIPTION

Currently, feedback instances are statically established and provisionedin different layers of a compute, storage, network, and managementinfrastructure. For each feedback instance provisioned in the layeredinfrastructure, data are collected, anomalies are analyzed, andactionable tasks are derived by a policy engine for enforcementcomponents to execute. When the infrastructure becomes more complex,such as with hundreds of thousands of feedback instances running totrack down problems and anomalies detected in both the virtual andphysical world, drilling down and verifying the true root cause of theseproblems and anomalies based upon advanced policy constructs will begreatly increased.

Concepts and technologies disclosed herein are directed to the dynamiccreation and management of ephemeral coordinated feedback instances toaddress the aforementioned problems and others that will become apparentafter reading this disclosure. According to embodiments of the conceptsand technologies disclosed herein, a new feedback instance can becreated automatically or semi-automatically when a need is identified. Afeedback instance model can be established based upon model templatesand policies by a feedback instance resolver and creator entity. Theobjective(s) of the model can be identified based upon one or morerequests. The target actors can be identified, input attributes can becollected, and analytic applications can be identified or created. Thefeedback instance model can be stored in a repository for later use. Afeedback instance can be instantiated based upon the feedback instancemodel to be handled by another entity referred to herein as a feedbackinstance orchestrator and controller (“FIOC”). The FIOC entity canretrieve the feedback instance model from the repository, implement themodel, verify the feedback instance model result, and activate a newfeedback instance based upon the feedback instance model in production.

Other concepts and technologies disclosed herein are directed topolicy-based modification of existing feedback instances. In accordancewith one aspect disclosed herein, a feedback instance change and upgraderesolver (“FICUR”) entity can modify feedback instance models based uponextensibility of one or more policies. More particularly, the FICURentity can examine an original objective of a feedback instance modeland can compare the original objective to a change/upgrade request. Theoriginal objective can be extended based upon one or more modelextensibility policies to specify a modified objective. Additionalattributes can be added to the feedback instance model based upon themodified objective. The realization of the modified feedback instancemodel can be handled by another entity referred to herein as a feedbackinstance orchestrator and controller (“FIOC”). The FIOC entity canretrieve the modified feedback instance model that the FICUR entitymodified and can implement the modified portion of the modified feedbackinstance model to an existing feedback instance in production.

Other concepts and technologies disclosed herein are directed to theescalation of feedback instances. In accordance with one aspectdisclosed herein, a policy driven feedback instance escalation resolver(“FIER”) entity can enable a dynamic method of escalating from anoriginal feedback instance to a different feedback instance in the samedomain or across different domains. The FIER can refine the objective ofthe original feedback instance. Based on the refined objective, the FIERcan search a plurality of active and non-active feedback interfacemodels to determine one or more possible candidates. The FIER can thenapply a distance calculation to obtain the highest score candidate. Amodel identifier is obtained from the highest score candidate.Realization of the feedback instance escalation can be handled by theFIOC. The FIOC can retrieve the model using the model identifierreceived from the FIER from a model repository. If the feedback instanceis in a non-active state, the FIER can re-activate the model first. TheFIER can then associate an escalation plan to the escalated active modelin runtime.

Other concepts and technologies disclosed herein are directed toconsultation among feedback instances. In accordance with one aspectdisclosed herein, a feedback instance consultation and interactionresolver (“FICIR”) entity can enable a dynamic way for a feedbackinstance to issue a consultation request to another feedback instance inthe same domain or across different domain(s). The FICIR entity canattempt to map the consultation request to an available applicationprogramming interface (“API”) published by one or more feedbackinstances. If an available API is found, the FICIR entity can record amethod of API invocation in an active feedback instance storage inassociation with a requesting feedback instance model. If no availableAPI is found, the FICIR entity can map the consultation request to aclosest match to a target feedback instance objective. Once matched, theFICIR entity can map the consultation request to an event type that thetarget feedback instance already supports. The FICIR entity can retrievean event response that the target feedback instance publishes. Aninteraction mechanism can be recorded back to both the requestingfeedback instance and the target feedback instance in the activefeedback instance store. Realization of the feedback instanceintercommunication can be handled by the FIOC entity. The FIOC entitycan receive one or more model identifiers from the FICIR. The FIOC canuse the model identifier(s) to retrieve the corresponding feedbackinstance model(s) from a model repository. The FIOC can enable/upgradean intercommunication path between the requesting feedback instance andthe target feedback instance.

Other concepts and technologies disclosed herein are directed tomultiple feedback instance inter-coordination to determine optimalactions. In accordance with one aspect disclosed herein, a feedbackinstances coordination and optimization resolver (“FICOR”) entity canreceive events from a group of feedback instances. The FICOR canleverage policies to develop a coordinated optimization plan to optimizeactions performed by the group of feedback instances. Realization of thecoordinated optimization plan can be handled by the FIOC. The FIOCentity can interact with all participating feedback instances in thegroup and can execute the coordinated optimization plan to optimizeactions performed by the group of feedback instances.

Other concepts and technologies disclosed herein are directed to modesof policy participation for feedback instances. A mode can represent adegree or level of guidance, constraints, and/or interactions providedby policy. Alternatively, or more generally, a mode can reflectdifferent policy guidance, constraints, and/or interactions. Inaccordance with one aspect disclosed herein, a feedback instance can beadjusted and executed at an optimized policy participation level mode(“PPLM”) based upon current feedback instance conditions. Explicitinvolvement of policy can be automatically or manually adjusted to asuitable mode based upon data associated with traffic conditions,computing resource conditions, network resource conditions, energyconditions, priority consideration, or a combination thereof.

While the subject matter described herein may be presented, at times, inthe general context of program modules that execute in conjunction withthe execution of an operating system and application programs on acomputer system, those skilled in the art will recognize that otherimplementations may be performed in combination with other types ofprogram modules. Generally, program modules include routines, programs,components, data structures, computer-executable instructions, and/orother types of structures that perform particular tasks or implementparticular abstract data types. Moreover, those skilled in the art willappreciate that the subject matter described herein may be practicedwith other computer systems, including hand-held devices, mobiledevices, wireless devices, multiprocessor systems, distributed computingsystems, microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers, routers, switches, other computingdevices described herein, and the like.

Turning now to FIG. 1, a block diagram illustrating aspects of afeedback instance architecture 100 will be described, according to anillustrative embodiment. A feedback instance, as used herein, is anintentional, directed feedback loop that can be utilized to effect, atleast in part, one or more policies in a cloud computing environment. Afeedback instance can include one or more targets, one or moreobjectives, and one or more inputs. A feedback instance can optionallyinclude a duration, a state, a history, or a combination thereof. Atarget can include one or more actors to be influenced by a feedbackinstance. An actor can include one or more components of the feedbackinstance architecture 100, one or more existing feedback instances, oneor more orchestrators, one or more controllers, one or more networkfunctions, traditional routers, traditional switches, traditionalfirewalls, servers, computing devices, storage devices, one or moreanalytic modules, one or more virtual functions (e.g., virtual networkfunctions, virtual system functions, and/or virtual applicationfunctions), and other components (not shown), and combinations thereof.An actor alternatively can include a scope of orchestration thatincludes operations performed by one or more components of the feedbackinstance architecture 100, one or more existing feedback instances, oneor more orchestrators, one or more controllers, one or more networkfunctions, one or more analytic modules, and/or other components of acloud computing environment. An objective can include one or more goalsto be achieved by a feedback instance, along with any pertinentdefinitions and scopes to meet the goal(s). An input can include anydata to be utilized by a feedback instance to at least partially achievean objective. A duration can include a lifespan of a feedback instance,a time-to-live (“TTL”) of a feedback instance, or other similarattributes. A state can include active, dormant, and reserved states. Itis contemplated that other states can be defined to accommodate otherscenarios. A history can include when a feedback instance was used.

The illustrated feedback instance architecture 100 includes one or morepolicy requestors 102, a policy engine (“PE”) 104, a policy repository106, one or more policy enforcement points 108, a feedback instancemodel repository 110, one or more other repositories 112, a feedbackinstance resolver and creator (“FIRC”) 114, a feedback instance changeand upgrade resolver (“FICUR”) 116, a feedback instance escalationresolver (“FIER”) 118, a feedback instance consultation and interactionresolver (“FICIR”) 120, a feedback instance coordination andoptimization resolver (“FICOR”) 122, a feedback instance mode operationmonitor and selector (“FIOM/FIMOS”) 124, a feedback instanceorchestrator and controller (“FIOC”) 126, and one or more feedbackinstances 128A-128N (collectively “feedback instances” 128). Each ofthese components and others will be described in detail below. Whileconnections are shown between some of the components illustrated in FIG.1, it should be understood that some, none, or all of the componentsillustrated in FIG. 1 can be configured to interact with one another tocarry out various operations described herein. Thus, it should beunderstood that FIG. 1 and the following description are intended toprovide a general understanding of a suitable environment in whichvarious aspects of embodiments can be implemented, and should not beconstrued as being limiting in any way.

The policy requestor(s) 102 can include any entity that can utilize oneor more policies. In some embodiments, the policy requestor 102 includesa component of the feedback instance architecture 100. In some otherembodiments, the policy requestor 102 includes an activated feedbackinstance. The policy requestor 102, in other embodiments, includes acustomer, a service provider, or other human or non-human entity. Thepolicy requestor(s) 102 can generate one or more policy requests 130directed to the PE 104. The policy request(s) 130 can be generatedautomatically by the policy requestor(s) 102, semi-automatically by thepolicy requestor(s) 102 along with input received from one or moreexternal sources such as one or more human operators, or manually inresponse to input received exclusively from one or more human operators.

The PE 104 can receive data associated with one or more events(illustrated as event data 132), data associated with one or moreoperational inquiries (illustrated as operational inquiry data 134),and/or data associated with an active monitoring process (illustrated asactive monitoring data 136). The policy requestor(s) 102 can communicatewith the PE 104 to retrieve at least a portion of the event data 132,the operational inquiry data 134, the active monitoring data 136, orsome combination thereof. In some embodiments, the policy requestor(s)102 can subscribe to at least a portion of the event data 132, theoperational inquiry data 134, the active monitoring data 136, or somecombination thereof. The policy requestor(s) 102 can subscribe directlyto the component that is the source of at least a portion of the eventdata 132, the operational inquiry data 134, the active monitoring data136, or some combination thereof.

The PE 104 can receive the policy request(s) 130 from the policyrequestor(s) 102 and can determine, through a policy decision process,whether to activate one or more existing policies, such as one or morepolicies 138A-138N (collectively “policies” 138), stored in the policyrepository 106. If the PE 104 determines that at least one of thepolicies 138 in the policy repository 106 should be activated inresponse to the policy request(s) 130, the PE 104 can retrieve at leastone of the policies 138 from the policy repository 106 and can instructthe policy enforcement point(s) 108 to enforce at least one of thepolicies 138. The policy enforcement point(s) 108 can be or can includeone or more orchestrators, controllers, application servers, otherservers, one or more of the feedback instances 128A-128N, combinationsthereof, and the like. If, however, the PE 104 determines that none ofthe policies 138 in the policy repository 106 should be activated inresponse to the policy request(s) 130, the PE 104 can determine that afeedback instance should be utilized, at least in part, to satisfy thepolicy request(s) 130. The PE 104 can communicate with the FIRC 114, theFICUR 116, the FIER 118, the FICIR 120, the FICOR 122, the FIOM/FIMOS124, the FIOC 126, or some combination thereof to utilize, at least inpart, one or more of the feedback instances 128 and/or create one ormore new feedback instances to satisfy the policy request(s) 130.

The PE 104 can communicate with the FIRC 114. The FIRC 114 can createone or more new feedback instance models for root cause identificationof problems and policy optimization purposes. In some embodiments, theFIRC 114 can create one or more new feedback instance models, whichmight be based, at least in part, upon one or more model templatesstored in the feedback instance model repository 110, or might begenerated without any model template. The FIRC 114 can create the newfeedback instance model(s) in response to a request received from the PE104. The FIRC 114 can identify one or more intentions/objectives for thenew feedback instance model. The FIRC 114 can identify one or moreactors to be utilized by the new feedback instance model. The FIRC 114can identify one or more input attributes to be collected by the newfeedback instance model. The FIRC 114 can identify one or moreapplications that should be utilized by the new feedback instance model.The FIRC 114 also can generate a feedback instance model identifier andcan associate the feedback instance model identifier with the newfeedback instance model. The FIRC 114 can store the new feedbackinstance model and the feedback instance model identifier in anon-active feedback instance model storage 140 of the feedback instancemodel repository 110. Realization of feedback instance models can behandled by the FIOC 126. The FIOC 126 can retrieve the new feedbackinstance model created by the FIRC 114, implement the new feedbackinstance model, verify that the new feedback instance model creates theappropriate model results, and can activate one or more new feedbackinstances in production based upon the new feedback instance model.

The PE 104 can communicate with the FICUR 116. The FICUR 116 can modifythe scope of one or more existing feedback instance models for rootcause identification of problems and policy optimization purposes. TheFICUR 116 can modify (e.g., change and/or upgrade) one or more existingfeedback instance models stored in the feedback instance modelrepository 110 based upon one or more model extensibility policies, suchas one or more of the policies 138 stored in the policy repository 106.The FICUR 116 can examine an original intention/objective of one or moreexisting feedback instance models and compare the originalintention/objective to a modification request received from the PE 104.The FICUR 116 can extend the original intention/objective of one or moreexisting feedback instance models based upon one or more modelextensibility policies. The FICUR 116 can add one or more attributesbased upon the modified/extended original intention/objective. The FICUR116 can utilize the same feedback instance model identifier as theoriginal feedback instance model prior to modification. Realization ofmodified feedback instance models can be handled by the FIOC 126. TheFIOC 126 can retrieve a modified feedback instance model created by theFICUR 116, implement the modified feedback instance model or implementthe modified portion of the modified feedback instance model, verifythat the modified feedback instance model creates the appropriate modelresults, and can activate one or more feedback instances or modify oneor more feedback instances currently in production based upon themodified feedback instance model.

The PE 104 can communicate with the FIER 118. The FIER 118 can enable adynamic process of escalating from an original feedback instance to adifferent feedback instance in the same domain or across differentdomains. The FIER 118 can convey one or more issues unresolved in onefeedback instance, such as one of the feedback instances 128, to anotheractive or non-active feedback instance. The FIER 118 can refine anexisting intention/objective of the original feedback instance or cancreate a new intention/objective for the original feedback instance.Based upon the refined or new intention/objective, the FIER 118 cansearch the non-active feedback instance model storage 140 and an activefeedback instance model storage 142 of the feedback instance modelrepository 110 to find any candidate feedback instance model(s) that canbe utilized to satisfy the refined or new intention/objective. The FIER118 can perform a distance calculation process to determine how closeeach candidate feedback instance model is to a model that shares therefined or new intention/objective. The FIER 118 can select thecandidate with the highest distance score (i.e., closest match) and canobtain the model identifier associated with that candidate and canprovide the model identifier to the FIOC 126. Realization feedbackinstance escalation can be handled by the FIOC 126. The FIOC 126 canretrieve the appropriate feedback instance model from the feedbackinstance model repository 110 using the model identifier received fromthe FIER 118. If the feedback instance model is in a non-active state,the FIER 118 can activate or re-activate the model for escalation.

The PE 104 can communicate with the FICIR 120. The FICIR 120 can buildor extend the interaction capability between two feedback instancemodels to allow a requesting feedback instance model to acquireinformation from another feedback instance model. The FICIR 120 canenable a dynamic process through which a requesting feedback instancecan issue a consultation request to another feedback instance in thesame domain or across different domains. The FICIR 120 can map aconsultation request to an available application programming interface(“API”) published by (e.g., exposed by) one or more feedback instances.If an API is found, a method of API invocation can be recorded in therequesting feedback instance model in the active feedback instance modelstorage 142. If no API is found, the FICIR 120 can map the consultationrequest to a closest target feedback instance intention/objective. Oncematched, the FICIR 120 can map the request to an event type that thetarget feedback instance already supports. The FICIR 120 can retrieve anevent response that the target feedback instance publishes. Theinteraction mechanism can be recorded back to both the requestingfeedback instance model and to the target feedback instance model in theactive feedback instance model storage 142. Realization of feedbackinstance intercommunication can be handled by the FIOC 126. The FIOC 126can retrieve the models from the feedback instance model repository 110.The FIOC 126 can then enable/upgrade an intercommunication path betweenthe requesting feedback instance and the target feedback instance.

The PE 104 can communicate with the FICOR 122. The FICOR 122 can receiveevents from a group of feedback instance and can leverage one or morepolicies, such as one or more of the policies 138, to develop acoordinated optimization plan. Realization of the coordinatedoptimization plan can be handled by the FIOC 126. The FIOC 126 caninteract with all participating feedback instances to execute thecoordinated optimization plan.

In a feedback loop construct, a set of components can interact with eachother in a deterministic manner. For example, in a feedback loopinstance that involves three actors such as a controller, a policyengine, and an analytic module, the analytic module can collect eventsand logs from the controller; the policy engine can detect one or moreanomalies; the policy engine also can subscribe to events and candeliver actionable instructions to the controller for adjustment toanomalies and/or events; and the controller can execute the action(s)and can log the result in a log entity. This pattern can be repeatedcontinuously until the feedback instance is deactivated. Thisdeterministic mechanism fails to support additional flexibility and/orefficiency of how the entire feedback instance should be executed basedon various conditions, especially the level ofparticipation/contribution from an external policy engine in a givenruntime condition.

The PE 104 can communicate with the FIOM/FIMOS 124. The FIOM/FIMOS 124can coordinate adjustment and execution of policy participation levelmode (“PPLM”) based upon current feedback instance conditions. Based ontraffic conditions, computing or network resource conditions, energyconditions and priority considerations, explicit involvement of policycan be automatically or manually tuned/adjusted to a suitable mode. A“mode,” as used herein, can represent a degree or level of guidance,constraints, required interactions, and/or the like provided by apolicy. Alternately, or more generally, modes may reflect different(alternative, in any respect) policy guidance, constraints, requiredinteractions, and/or the like.

Turning now to FIGS. 2A-2B, aspects of a method 200 for operating thefeedback instance architecture will be described, according to anillustrative embodiment. It should be understood that the operations ofthe methods disclosed herein are not necessarily presented in anyparticular order and that performance of some or all of the operationsin an alternative order(s) is possible and is contemplated. Theoperations have been presented in the demonstrated order for ease ofdescription and illustration. Operations may be added, omitted, and/orperformed simultaneously, without departing from the scope of theconcepts and technologies disclosed herein.

It also should be understood that the methods disclosed herein can beended at any time and need not be performed in its entirety. Some or alloperations of the methods, and/or substantially equivalent operations,can be performed by execution of computer-readable instructions includedon a computer storage media, as defined herein. The term“computer-readable instructions,” and variants thereof, as used herein,is used expansively to include routines, applications, applicationmodules, program modules, programs, components, data structures,algorithms, and the like. Computer-readable instructions can beimplemented on various system configurations including single-processoror multiprocessor systems, minicomputers, mainframe computers, personalcomputers, hand-held computing devices, microprocessor-based,programmable consumer electronics, servers, routers, switches,combinations thereof, and the like.

Thus, it should be appreciated that the logical operations describedherein are implemented (1) as a sequence of computer implemented acts orprogram modules running on a computing system and/or (2) asinterconnected machine logic circuits or circuit modules within thecomputing system. The implementation is a matter of choice dependent onthe performance and other requirements of the computing system.Accordingly, the logical operations described herein are referred tovariously as states, operations, structural devices, acts, or modules.These states, operations, structural devices, acts, and modules may beimplemented in software, in firmware, in special purpose digital logic,and any combination thereof. As used herein, the phrase “cause aprocessor to perform operations” and variants thereof is used to referto causing a processor, a processor one or more computing systems,devices, engines, switches, routers, or components disclosed herein toperform operations. It should be understood that the performance of oneor more operations may include operations executed by one or morevirtual processors at the instructions of one or more of theaforementioned hardware processors.

The method 200 will be described with reference to FIGS. 2A-2B andfurther reference to FIG. 1. The method 200 begins at operation 202,where the PE 104 receives at least a portion of the event data 132, theoperational inquiry data 134, and/or the active monitoring data 136.From operation 202, the method 200 proceeds to operation 204, where thePE 104 receives the policy request 130 from the policy requestor 102.

From operation 204, the method 200 proceeds to operation 206, where thePE 104 determines whether one or more policies exist, such as one ormore of the policies 138 stored in the policy repository 106, that canbe utilized to satisfy the policy request 130. If, at operation 208, oneor more policies are available, the method 200 proceeds to operation210, where the PE 104 retrieves, from the policy repository 106, the oneor more policies to satisfy the policy request 130. From operation 210,the method 200 proceeds to operation 212, where the PE 104 instructs oneor more of the policy enforcement points 108 to enforce the one or morepolicies. As described above, the policy enforcement point(s) 108 can beor can include one or more orchestrators, controllers, applicationservers, other servers, one or more of the feedback instances 128A-128N,combinations thereof, and the like. From operation 212, the method 200proceeds to operation 214, where the policy enforcement point(s) 108enforce the one or more policies. From operation 214, the method 200proceeds to operation 216. The method 200 ends at operation 216.

If, at operation 208, one or more policies are not available, the method200 proceeds to operation 218 shown in FIG. 2B. At operation 218, the PE104 determines whether one or more existing feedback instances should bemodified by the FICUR 116 to satisfy the policy request 130. If so, themethod 200 proceeds to operation 220, which represents a methodperformed, at least in part, by the FICUR 116 to modify one or moreexisting feedback instances as illustrated and described with referenceto FIG. 6. If not, the method 200 proceeds to operation 222.

One non-limiting example of the method for modifying an existingfeedback instance model will now be described. In this example, afeedback instance is utilized to monitor one or more anomalies of a setof home devices and related virtual network functions (“VNFs”)associated with the set of home devices in a network. The actors of thefeedback instance can include the physical home devices, an accesscontrol server for the physical home devices, the VNFs associated withthe physical home devices, a virtual function controller, an analyticsmodule, and the PE 104. In this example, the feedback instance can focuson Internet traffic performance. A few anomalies are detected whichimply unusual video pattern to clog an access network. The attributesbeing collected did not include over the top video program usageparameters. Therefore, the normal way to deal with the issue is topublish a video congestion event to a trouble ticket system which mustbe accomplished via a manual intervention process. However, using thefeedback instance, the PE 104 can automatically determine to extend thedata collection scope with the inclusion of over-the-top usageparameters. The automation of the PE 104 further determines to add avideo pattern analytic module to the feedback instance, since the PEs104 inputs seem to indicate this need, which justifies the additionalfeedback loop extent and effort. Thus, the feedback instance can bemodified to take on the new task with no manual intervention.

At operation 222, the PE 104 determines whether the policy request 130should be escalated to an existing feedback instance. If so, the method200 proceeds to operation 224, which represents a method performed, atleast in part, by the FIER 118, to escalate an existing feedbackinstance as illustrated and described with reference to FIG. 8. If not,the method 200 proceeds to operation 226.

One non-limiting example for escalating to an existing feedback instancewill now be described. In this example, an original feedback instanceinvolves three actors, namely a software-defined network (“SDN”)controller, the PE 104, and an analytics module. The original feedbackinstance can enable the analytics module to identify one or moreanomalies. The original feedback instance also can enable the PE 104 torecommend one or more mitigation rules. The original feedback instancealso can instruct the SDN controller to execute the mitigation rule(s).In this example, an anomaly is identified but the PE 104 determines thatthe scope of the resolution or needed actions might be beyond what thefeedback instance can handle. The possible fixes for this scenario areto either modify the existing feedback instance (such as by utilizingthe method for modifying the feedback instance briefly described aboveand in further detail herein below) or to escalate to another feedbackinstance to handle the anomaly. The PE 104 can locate the other feedbackinstance to perform the escalation process. In this example, the otherfeedback instance can involve four actors, including the SDN controller,the PE 104, and the analytics module of the feedback instance. Thefourth actor can be an orchestrator that has the capability ofperforming cross-domain validation. For example, in this scenario tocompare the anomaly to one or more similar patterns in another region.If the pattern matches to what is known in another region, theorchestrator can facilitate a signature migration process to theoriginal feedback instance. If, however, the pattern does not match, anew signature should be created for future references in the originalfeedback instance. After the escalation process satisfactorilycompletes, control may be passed back to the original feedback instance.

At operation 226, the PE 104 determines whether a new feedback instanceshould be created to satisfy the policy request 130. If so, the method200 proceeds to operation 228, which represents a method performed, atleast in part, by the FIRC 114, to create a new feedback instance modelupon which the new feedback instance can be based as illustrated anddescribed with reference to FIG. 4. If not, the method 200 proceeds tooperation 230.

One non-limiting example of the method for creating a new feedbackinstance model will now be described. In this example, a first feedbackinstance can monitor layer 3 network traffic patterns. The firstfeedback instance can include a physical router (e.g., a CISCO brandrouter), a virtual router, and the PE 104 as actors. Data attributes canbe collected and documented in a feedback instance model template storedin the feedback instance model repository 110. The model template canprovide one layer of abstraction so that future physical router/virtualrouter of different brands can reuse the model template in certain way.Installation personnel can install a set of other routers (e.g., JUNIPERbrand routers). In this example, the personnel does not have time toretool the model template and create new feedback instance(s) particularto the JUNIPER routers. After installation of the JUNIPER routers,network operations personnel can issue a request to monitor slow networkresponse time. The PE 104 might fail to locate an active or inactivefeedback instance to take on the request to monitor slow networkresponse time and, in response, can determine to create a new feedbackinstance (“second feedback instance”). The PE 104 can leverage theexisting model template for the first feedback instance but can readjustthe involved actors and dynamically create the second feedback instancein the FI model repository 110. The FIOC 126 can realize/instantiate thesecond feedback instance in runtime. Analysis from the second feedbackinstance can then be passed back to the network operations personnel.The second feedback instance can continue to operate.

At operation 230, the PE 104 determines whether the PE 104 shouldconsult with one or more other feedback instances to satisfy the policyrequest 130. If so, the method 200 proceeds to operation 232, whichrepresents a method performed, at least in part, by the FICIR 120 toenable intercommunication among feedback instances to satisfy the policyrequest 130 as illustrated and described with reference to FIG. 10. Ifnot, the method 200 proceeds to operation 234.

One non-limiting example of the method for a first feedback instance toconsult with a second feedback instance will be described. In thisexample, the first feedback instance can monitor virtual machine (“VM”)usage. The first feedback instance can detect heavy application usagesfor a set of VMs. Under normal operation, the first feedback instancecan expand VM instances on the same hardware or relocate VMs to newhardware. Although users will not notice the difference, the networkprocessors will be tied up for a time, which might slow down othermaintenance related activities. In order to ensure the anomaly should behandled in this manner, the first feedback instance can consult with thePE 104 for further instruction. The PE 104 can suggest a consultationrequest to a second feedback instance, which includes an applicationcontroller, one or more VNFs, an analytics module, and the PE 104. Thesecond feedback instance can response with the message “Demand forApplication #1-Application # N will soon reduce by a factor of 80% in 15minutes.” As a result of this consultation, the first feedback instancecan determine to only migrate a small set of VMs to a new hardware andleave the rest intact.

At operation 234, the PE 104 determines whether the PE 104 shouldcoordinate a solution via an optimization plan that involves multiplefeedback instances. If so, the method 200 proceeds to operation 236,which represents a method performed, at least in part, by the FIOM/FIMOS124 to coordinate a solution via an optimization plan that involvesmultiple feedback instances as illustrated and described with referenceto FIG. 12. If not, the method 200 proceeds to operation 216 shown inFIG. 2A. The method 200 ends at operation 216. Alternatively, the PE 104may issue an error or other notification to suggest that the policyrequest 130 cannot currently be satisfied, in response to which anoperator or other entity can intervene.

One non-limiting example of the method for coordinating a solution amongfeedback instances will be described. In this example, a first feedbackinstance for VM usage management can include three actors, namely anorchestrator, a cloud resource controller, and a PE 104. The firstfeedback instance can be used to detect VM usage issues. In this examplescenario, an anomaly was detected that an event is ready to be publishedto create new VMs to ease off the capacity glitches. However, thisscenario is happening at an unusual time period (e.g., at 4:30 PM whichnormally would be a time for the traffic to go down, not up). The PE 104can make a dynamic decision to coordinate with a second feedbackinstance focused on security to coordinate with the first feedbackinstance to monitor the situation. The second feedback instance candetect a Denial of Service (“DNS”) attack. Instead of creating new VMinstances via the first feedback instance, the security threats caninstead be mitigated via the second feedback instance.

Turning now to FIG. 3, a block diagram illustrating further aspects ofthe feedback instance architecture 100′ will be described, according toan embodiment. The illustrated feedback instance architecture 100′includes the policy requestor 102, the PE 104, the policy repository106, the FIRC 114, the feedback instance model repository 110, and theFIOC 126. Each of these components and others will be described indetail below. While connections are shown between some of the componentsillustrated in FIG. 3, it should be understood that some, none, or allof the components illustrated in FIG. 3 can be configured to interactwith one another to carry out various operations described herein. Thus,it should be understood that FIG. 3 and the following description areintended to provide a general understanding of a suitable environment inwhich various aspects of embodiments can be implemented, and should notbe construed as being limiting in any way.

The policy requestor 102 can generate the policy request 130 directed tothe PE 104. The PE 104 can receive the policy request 130 from thepolicy requestor 102 and can determine, through a policy decisionprocess, whether or not a policy exists that can be utilized to satisfythe policy request 130. If so, the PE 104 can activate the policy andcan instruct the policy enforcement point(s) 108 to enforce the policy.

The illustrated PE 104 includes a PE processing unit 300, a PE memoryunit 302, and a PE module 304. The PE processing unit 300 can be or caninclude one or more central processing units (“CPUs”) configured withone or more processing cores. The PE processing unit 300 can be or caninclude one or more graphics processing units (“GPUs”) configured toaccelerate operations performed by one or more CPUs, and/or to performcomputations to process data, and/or to execute computer-executableinstructions of one or more application programs, operating systems,and/or other software that may or may not include instructionsparticular to graphics computations. In some embodiments, the PEprocessing unit 300 can be or can include one or more discrete GPUs. Insome other embodiments, the PE processing unit 300 can be or can includeCPU and GPU components that are configured in accordance with aco-processing CPU/GPU computing model, wherein the sequential part of anapplication executes on the CPU and the computationally-intensive partis accelerated by the GPU. The PE processing unit 300 can be or caninclude one or more field-programmable gate arrays (“FPGAs”). The PEprocessing unit 300 can be or can include one or more system-on-chip(“SoC”) components along with one or more other components, including,for example, the PE memory unit 302. In some embodiments, the PEprocessing unit 300 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OpenMultimedia Application Platform (“OMAP”) SoCs, available from TEXASINSTRUMENTS of Dallas, Tex.; one or more customized versions of any ofthe above SoCs; and/or one or more proprietary SoCs. The PE processingunit 300 can be or can include one or more hardware componentsarchitected in accordance with an ARM architecture, available forlicense from ARM HOLDINGS of Cambridge, United Kingdom. Alternatively,PE processing unit 300 can be or can include one or more hardwarecomponents architected in accordance with an x86 architecture, such anarchitecture available from INTEL CORPORATION of Mountain View, Calif.,and others. Those skilled in the art will appreciate the implementationof the PE processing unit 300 can utilize various computationarchitectures, and as such, the PE processing unit 300 should not beconstrued as being limited to any particular computation architecture orcombination of computation architectures, including those explicitlydisclosed herein.

The PE memory unit 302 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the PE memory unit302 include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, random access memory (“RAM”), read-only memory (“ROM”),Erasable Programmable ROM (“EPROM”), Electrically Erasable ProgrammableROM (“EEPROM”), flash memory or other solid state memory technology,CD-ROM, digital versatile disks (“DVD”), or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storedata and which can be accessed by the PE processing unit 300. The PEmodule 304 can be stored in the PE memory unit 302.

The PE module 304 can be executed by the PE processing unit 300 toperform operations in response to the policy request 130. Moreparticularly, the PE module 304 can execute operations of a policydecision process through which the PE module 304 determines whether toactivate one or more existing policies, such as one or more of thepolicies 138, stored in the policy repository 106. If the PE module 304determines that at least one of the policies 138 in the policyrepository 106 should be activated in response to the policy request130, the PE module 304 can retrieve the one or more policies 138 fromthe policy repository 106 and can instruct the policy enforcementpoint(s) 108 to enforce the one or more policies 138. If, however, thePE module 304 determines that none of the policies 138 in the policyrepository 106 should be activated in response to the policy request130, the PE module 304 can determine that a feedback instance should becreated to satisfy the policy request 130, and so can generate afeedback instance creation request 306 directed to the FIRC 114.

The PE 104 can generate the feedback instance creation request 306based, at least in part, upon feedback instance creation decisioncriteria 308. The feedback instance creation decision criteria 308 caninclude one or more targets, one or more intentions/objectives, one ormore inputs, and/or one or more operational attributes such as durationand/or state. The PE 104 can examine the policy request 130 and theinitial policy decision to generate the feedback instance creationdecision criteria 308. For instance, the PE 104 can define theobjective(s) for a feedback instance. The PE 104 also can define thetarget(s), the input(s), and the operational attribute(s).

The illustrated FIRC 114 includes an FIRC processing unit 310, an FIRCmemory unit 312, an external interface subsystem (“EIS”) module 314, apolicy adaptive subsystem (“PAS”) module 316, and a model establishmentsubsystem (“MES”) module 318. The EIS module 314, the PAS module 316,and the MES module 318 can be stored in the FIRC memory unit 312 and canbe executed by the FIRC processing unit 310 to cause the FIRC 114 toperform operations to create feedback instance models in accordance withembodiments disclosed herein.

The FIRC processing unit 310 can be or can include one or more CPUsconfigured with one or more processing cores. The FIRC processing unit310 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FIRC processing unit 310 can beor can include one or more discrete GPUs. In some other embodiments, theFIRC processing unit 310 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFIRC processing unit 310 can be or can include one or more FPGAs. TheFIRC processing unit 310 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FIRC memory unit 312. In some embodiments, the FIRCprocessing unit 310 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FIRC processing unit 310 can be or can include oneor more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FIRC processing unit 310 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FIRC processing unit 310can utilize various computation architectures, and as such, the FIRCprocessing unit 310 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FIRC memory unit 312 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FIRC memory unit312 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FIRC processing unit 310.

The EIS module 314 can receive and examine feedback instance creationrequests, such as the illustrated feedback instance creation request 306received from the PE 104. The EIS module 314 can receive and examineother feedback instance creation requests from other participatingactors. The EIS module 314 can provide a feedback instance modelrealization request 320 to the FIOC 126.

The PAS module 316 can examine the feedback instance creation request306 using one or more policy rules to define one or more objectives fora new feedback instance model. The PAS module 316 can then use thedefined model objective(s) to derive one or more model target actors,one or more model input attributes as well as associated real-timefeedback instance establishment and operation visualizationattributes/policies. Real-time feedback instance establishment andoperation visualization attributes/policies can indicate, influence, orcontrol various aspects of establishment and visualization. Policycriteria (as attributes) can be defined under what conditions aparticular feedback instance can be activated. Examples of theseattributes include, but are not limited, location, time of day, theintended uses (e.g., organization or security), and computing resourceconstraints of involved actors. Each of the feedback instances can thenbe visualized by network/system operation personnel. Attributes in thisregard can include, but are not limited to, the operation organization,security level, real-time update frequency (e.g., every second, everyminute, on-demand), drill down level, history log duration, and thelike. The MES module 318 can create a new feedback instance model basedupon output of the PAS module 316 and can provide the new feedbackinstance model along with an associated model identifier to the feedbackinstance model repository 110 for storage as a non-active feedbackinstance model in the non-active feedback instance model storage 140 ofthe feedback instance model repository 110. Upon or after activation ofone or more new feedback instances based upon the new feedback instancemodel, the model identifier of the new feedback instance model can bemoved to the active feedback instance model storage 142 of the feedbackinstance model repository 110.

The FIRC 114, in some embodiments, is implemented as a policyapplication executable by the PE processing unit 300 of the PE 104. Inother embodiments, such as the illustrated embodiment, the FIRC 114 isimplemented as an external entity that is in communication with the PE104 to support the creation of feedback instance models. A new feedbackinstance model creation request, such as the feedback instance creationrequest 306, can be received from the PE 104, although in someembodiments, the FIRC 114 can subscribe to requests directly from anactor external to the PE 104.

The illustrated FIOC 126 is located downstream from the FIRC 114 in theillustrated feedback instance architecture 100. The FIOC 126 can respondto the feedback instance model realization request 320 received from theFIRC 114 to realize—that is, to put into production—the feedbackinstance model identified in the feedback instance model realizationrequest 320. The FIOC 126 can function as a supporting entity to theFIRC 114. In some embodiments, the functionality of the FIOC 126 iscombined with the functionality of the FIRC 114. In other embodiments,such as the illustrated embodiment, the FIOC 126 is implemented as anexternal entity that is in communication with the FIRC 114.

The illustrated FIOC 126 includes an FIOC processing unit 322, an FIOCmemory unit 324, an actor interface subsystem (“AIS”) module 326, a flowexecution subsystem (“FES”) module 328, and a verification andactivation subsystem (“VAS”) module 330. The AIS module 326, the FESmodule 328, and the VAS module 330 can be stored in the FIOC memory unit324 and can be executed by the FIOC processing unit 322 to cause theFIOC 126 to perform operations described herein.

The FIOC processing unit 322 can be or can include one or more CPUsconfigured with one or more processing cores. The FIOC processing unit322 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FIOC processing unit 322 can beor can include one or more discrete GPUs. In some other embodiments, theFIOC processing unit 322 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFIOC processing unit 322 can be or can include one or more FPGAs. TheFIOC processing unit 322 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FIOC memory unit 324. In some embodiments, the FIOCprocessing unit 322 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FIOC processing unit 322 can be or can include oneor more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FIOC processing unit 322 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FIOC processing unit 322can utilize various computation architectures, and as such, the FIOCprocessing unit 322 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FIOC memory unit 324 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FIOC memory unit324 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FIOC processing unit 322.

The MS module 326 can receive the feedback instance model realizationrequest 320 from the FIRC 114. The AIS module 326 can retrieve afeedback instance model from the non-active feedback instance modelstorage 140 stored in the feedback instance model repository 110 inaccordance with a model identifier supplied in the feedback instancemodel realization request 320. The MS module 326 can interact with oneor more actors defined in the feedback instance model to realize one ormore of the feedback instances 128.

The FES module 328 can decompose a feedback instance model retrievedfrom the feedback instance model repository 110 and can direct the MSmodule 326 in an operation-by-operation manner to realize the model.Realizing the model can include instantiating the model for a particularfeedback instance. Some operations for realizing the model can include,but are not limited to, (1) examine identified analytic module instanceand then configure data collection algorithm to be used and eventpublication rules; (2) examine all other actors (e.g., one actor at atime) to configure needed policies/rules to each actor to make thefeedback instance complete; and (3) after operation (2), the feedbackinstance is created but still in an inactive mode until the feedbackinstance passes a verification process.

One operation in the flow can be enabling of one or more visualizationpolicies so that the feedback instance 128 can be visually observed byoperations personnel. When a feedback instance is activated, thefeedback instance can operate as a standalone feedback instancecontinuously. However, every feedback instance can be managed so thatoperations personnel can visualize the current condition of the feedbackinstance and the associated status of the feedback instance.Visualization policies can be defined to identify who can view thisinformation, how frequently the information can be used to update themonitoring dashboard, and how many levels the policy allows operationpersonnel to drill down to view finer grain details.

The VAS module 330 can perform one or more verification operationsbefore activating the feedback instance 128 in a production mode. Forexample, verification operations (prior to activation of the feedbackinstance) can include, but are not limited to, conflict detection,various heuristic and/or algorithmic validation checks, governancechecks, and the like. Operation groups such as network operation center(“NOC”), information technology (“IT”) operation personnel, Internetoperation center (“IOC”), and others might have an interest in verifyingthat the feedback instance is acceptable. Additional verificationoperations might be to ensure all participating actors are up andrunning, to ensure all activation policies are met (e.g., time of day,security level, location, and the like), and to ensure no conflictingfeedback instance(s) is/are present. The VAS module 330 also can performone or more activation operations to activate the feedback instance 128in the production mode.

The FIOC 126 can be implemented as an orchestration application within aservice and/or resource orchestrator or can be implemented as anexternal entity in communication with such an orchestrator. When theFIOC 126 is implemented as an external entity to the orchestrator, theFIOC 126 can leverage the existing interface infrastructure of theorchestrator.

Turning now to FIG. 4, aspects of a method 400 for creating and managingfeedback instances in the feedback instance architecture 100′ will bedescribed, according to an illustrative embodiment. The method 400begins at operation 402, where the PE 104 receives the policy request130 from the policy requestor 102. From operation 402, the method 400proceeds to operation 404, where the PE 104 receives the feedbackinstance creation decision criteria 308. From operation 404, the method400 proceeds to operation 406, where the PE 104 generates the feedbackinstance creation request 306 in response to the policy request 130 andbased, at least in part, upon the feedback instance creation decisioncriteria 308. From operation 406, the method 400 proceeds to operation408, where the PE 104 provides the feedback instance creation request306 to the FIRC 114.

From operation 408, the method 400 proceeds to operation 410, where theFIRC 114 examines the feedback instance creation request 306 todetermine one or more objectives to be met by a new feedback instancemodel. From operation 410, the method 400 proceeds to operation 412,where the FIRC 114 builds a detailed specification for the new feedbackinstance model using one or more feedback instance building policies.Feedback instance building policies can include any useful and variousconditional policies that will help enable the construction of feedbackinstances. For example, for a particular security related feedback, thefeedback instance might have to include one of the security relatedtools such as virus detection function, denial of service (“DoS”) attackmodule, deep packet inspection (“DPI”), and this can be indicated andenforced via a building policy. For a cloud resource feedback instance,a building policy can include a cloud controller and researchorchestrator in the building policies. The building policies can suggesta minimal set of actors to form the feedback instance. There will beadditional rules to suggest in what situation additional actors may needto participate. The FIRC 114 can use the requested “content” to matchthe intent. Once the intent is matched, the FIRC 114 can look into abuilding policy repository (as part of the other repositories 112) toselect the most suitable building policy/policies to be used as theblueprint for constructing the feedback instance.

From operation 412, the method 400 proceeds to operation 414, where theFIRC 114 creates a new feedback instance model in accordance with thedetailed specification built at operation 412. From operation 414, themethod 400 proceeds to operation 416, where the FIRC 114 stores the newfeedback instance model in the non-active feedback instance modelstorage 140 of the feedback instance model repository 110 in associationwith a unique feedback instance model identifier that identifies the newfeedback instance model for later retrieval.

From operation 416, the method 400 proceeds to operation 418, where theFIOC 126 receives the feedback instance model realization request 320from the FIRC 114. From operation 418, the method 400 proceeds tooperation 420, where the FIOC 126 retrieves the new feedback instancemodel from the feedback instance model repository 110 using the uniquefeedback instance model identifier received in the feedback instancemodel realization request 320. From operation 420, the method 400proceeds to operation 422, where the FIOC 126 performs feedback instancemodel realization operations to realize a new feedback instance, such asthe feedback instance 128, based upon the new feedback instance model.From operation 422, the method 400 proceeds to operation 424, where theFIOC 126 verifies the new feedback instance. From operation 424, themethod 400 proceeds to operation 426, where the FIOC 126 activates thenew feedback instance to put the new feedback instance into productionand monitors the new feedback instance based upon one or moreoperational parameters. Operational parameters can include monitoringdetails and any other factors or considerations useful in day-to-dayoperations of feedback instances. The operational parameters caninclude, but are not limited to, when the feedback instance can berunning, any location limitations or other limitations or constraintsthat might apply, which operational organizations can visually orotherwise see and monitor these feedback instances, who can suspend andresume these feedback instances, under what conditions the feedbackinstance should pause operation and request intervention, and the like.

From operation 426, the method 400 proceeds to operation 428. The method400 ends at operation 428.

Turning now to FIG. 5, a block diagram illustrating further aspects ofthe feedback instance architecture 100″ will be described, according toan illustrative embodiment. The illustrated feedback instancearchitecture 100 includes the policy requestor 102, the PE 104, thepolicy repository 106, the FICUR 116, the feedback instance modelrepository 110, and the FIOC 126. Each of these components and otherswill be described in detail below. While connections are shown betweensome of the components illustrated in FIG. 5, it should be understoodthat some, none, or all of the components illustrated in FIG. 5 can beconfigured to interact with one another to carry out various operationsdescribed herein. Thus, it should be understood that FIG. 5 and thefollowing description are intended to provide a general understanding ofa suitable environment in which various aspects of embodiments can beimplemented, and should not be construed as being limiting in any way.

The PE 104 can receive the policy request 130 from the policy requestor102 and can determine, through a policy decision process, whether or nota policy exists that can be utilized to satisfy the policy request 130.If so, the PE 104 can activate the policy and can instruct one or morepolicy enforcement points 108 to enforce the policy.

The illustrated PE 104 includes the PE processing unit 300, the PEmemory unit 302, and the PE module 304 described above with reference toFIG. 3. The PE module 304 can be executed by the PE processing unit 300to perform operations in response to the policy request 130. Moreparticularly, the PE module 304 can execute operations of a policydecision process through which the PE module 304 determines whether toactivate one or more existing policies, such as one or more of thepolicies 138 stored in the policy repository 106. If the PE module 304determines that at least one of the policies 138 in the policyrepository 106 should be activated in response to the policy request130, the PE module 304 can retrieve one or more of the policies 138 fromthe policy repository 106 and can instruct the policy enforcementpoint(s) 108 to enforce the one or more policies 138. If, however, thePE module 304 determines that none of the policies 138 in the policyrepository 106 should be activated in response to the policy request130, the PE module 304 can determine whether an existing, activefeedback instance, such as an original feedback instance 500, can beutilized to satisfy the policy request 130. If an existing, activefeedback instance can be utilized to satisfy the policy request 130, thePE 104 can associate that existing, active feedback instance with thepolicy request 130. For example, if the PE 104 determines that theoriginal feedback instance 500 can be utilized to satisfy the policyrequest 130, the PE 104 can associate the original feedback instance 500with the policy request 130. If, however, the original feedback instance500 cannot be utilized to satisfy the policy request 130, the PE 104 canidentify one or more deficiencies in the original feedback instance 500that can be cured via a modification process disclosed herein.

The illustrated original feedback instance 500 includes a plurality oforiginal feedback instance actors (illustrated as OFI actor_1-OFIactor_N) 502A-502N, although the original feedback instance 500 mightinclude a different number of actors. As used herein, the term“original” with regard to a feedback instance refers to an existing,active feedback instance prior to any modifications. Moreover, it shouldbe understood that a modified feedback instance can be further modifiedto accommodate additional policy requests and can be modified to revertback to a previous state. For example, the original feedback instance500 can be modified to add a new actor in response to the policy request130 and later can be modified to remove the new actor to revert back toa state indicative of the original feedback instance 500.

The original feedback instance 500 might be deficient in one or moreaspects. The original feedback instance 500 might be deficient in anumber of actors and/or type of actor. In some embodiments, the originalfeedback instance 500 includes a particular actor but can be deficientin that the particular actor is inactive. Alternatively, in otherembodiments, the original feedback instance 500 does not include anactor and, in this manner, is deficient. The original feedback instance500 might be deficient in a number of inputs and/or type of inputs. Theoriginal feedback instance 500 might be deficient in a type or number ofoperational parameters and/or values associated therewith. The originalfeedback instance 500 might be deficient in one or more applications(e.g., analytics application(s)), and might benefit from or require theaddition of one or more applications and/or the removal of one or moreapplications to cure a deficiency. The original feedback instance 500might need changes to other attributes such as, but not limited, tocapacity, traffic, and the like.

The PE 104 can generate a feedback instance modification request 504directed to the FICUR 116 to notify the FICUR 116 of one or moredeficiencies in the original feedback instance 500. The illustratedFICUR 116 includes an FICUR processing unit 506, an FICUR memory unit508, an FICUR EIS module 510, and a change/upgrade subsystem (“CUS”)module 512. The FICUR EIS module 510 and the CUS module 512 can bestored in the FICUR memory unit 508 and can be executed by the FICURprocessing unit 506 to cause the FICUR 116 to perform operations tomodify feedback instances in accordance with embodiments disclosedherein.

The FICUR processing unit 506 can be or can include one or more CPUsconfigured with one or more processing cores. The FICUR processing unit506 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FICUR processing unit 506 can beor can include one or more discrete GPUs. In some other embodiments, theFICUR processing unit 506 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFICUR processing unit 506 can be or can include one or more FPGAs. TheFICUR processing unit 506 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FICUR memory unit 508. In some embodiments, the FICURprocessing unit 506 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FICUR processing unit 506 can be or can includeone or more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FICUR processing unit 506 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FICUR processing unit 506can utilize various computation architectures, and as such, the FICURprocessing unit 506 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FICUR memory unit 508 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FICUR memory unit508 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FICUR processing unit 506.

The FICUR EIS module 510 can receive and examine feedback instancemodification requests, such as the illustrated feedback instancemodification request 504 received from the PE 104. The FICUR EIS module510 can receive and examine other feedback instance modificationrequests from other participating actors. The majority of feedbackinstance modification requests will likely come from the PE 104.However, in some cases, participating actors can also contribute to therequest process. For example, in a scale up feedback instance, aresource controller might signal the FICUR EIS module 510 that the VMspin-off process might become too frequent and certain thresholds mightneed to be readjusted without needing to modify the entire feedbackinstance. In this case, FICUR may decide to make some modification ofconfiguration parameters while keeping the feedback instance running.The FICUR EIS module 510 can provide a feedback instance modificationrealization request 514 to the FIOC 126 so that the FIOC 126 can executeone or more operations to upgrade or change the original feedbackinstance 500.

The CUS module 512 can examine the feedback instance modificationrequest 504 using one or more policy rules to modify one or more modelobjectives of the feedback instance model upon which the originalfeedback instance 500 is based. The CUS module 512 can then utilize themodified model objective(s) to modify a specification associated withthe feedback instance model to create a modified feedback instancemodel. Modification to the specification can add or reduce model targetactors, model input attributes, associated operation(s), and/orvisualization attributes/policies. The CUS module 512 can provide themodified feedback instance model and the associated modifiedspecification along with a model identifier to the feedback instancemodel repository 110. The CUS module 512 can store the modified feedbackinstance model in the active feedback instance model storage 142 of thefeedback instance model repository 110.

The FICUR 116, in some embodiments, is implemented as a policyapplication executable by the PE processing unit 300 of the PE 104. Inother embodiments, such as the illustrated embodiment, the FICUR 116 isimplemented as an external entity that is in communication with the PE104 to support the modification of feedback instance models. A feedbackinstance modification request, such as the feedback instancemodification request 504, can be received from the PE 104, although insome embodiments, the FICUR 116 can subscribe to requests directly froman actor external to the PE 104.

The illustrated FIOC 126 is located downstream from the FICUR 116 in thefeedback instance architecture 100. The FIOC 126 can respond to thefeedback instance modification realization request 514 received from theFICUR 116 to realize—that is, to put into production—the feedbackinstance model identified via a model identifier in the feedbackinstance modification realization request 514. The FIOC 126 can functionas a supporting entity to the FICUR 116. In some embodiments, thefunctionality of the FIOC 126 is combined with the functionality of theFICUR 116. In other embodiments, such as the illustrated embodiment, theFIOC 126 is implemented as an external entity that is in communicationwith the FICUR 116.

The illustrated FIOC 126 includes the FIOC processing unit 322, the FIOCmemory unit 324, the AIS module 326, the FES module 328, and the VASmodule 330. The AIS module 326, the FES module 328, and the VAS module330 can be stored in the FIOC memory unit 324 and can be executed by theFIOC processing unit 322 to cause the FIOC 126 to perform realizationoperations described herein.

The AIS module 326 can receive the feedback instance modificationrealization request 514 from the FICUR 116. The AIS module 326 canretrieve the modified instance model from the active feedback instancemodel storage 142 stored in the feedback instance model repository 110utilizing the model identifier supplied in the feedback instancemodification realization request 514.

The FES module 328 can decompose the modified feedback instance modelretrieved from the feedback instance model repository 110 and can directthe AIS module 326 in an operation-by-operation manner to perform arealization process using the modified feedback instance model to deploya modified feedback instance 516. The AIS module 326 can interact withone or more actors (illustrated as MFI actor_1-MFI actor_N) 518A-518Ndefined in the modified feedback instance 516 to establish connectivityas part of the realization process. The MFI actors 518A-518N can includeactors that were inactive or otherwise unavailable in the originalfeedback instance 500. Also as part of the realization process, the AISmodule 326 can negotiate with a data collection actor to ensureadditional feedback instance data attributes can be obtained. As anexample, assuming that a virtual router can provide one hundredattributes for performance monitoring, however, only ninety of theattributes are frequently used and the remaining ten include a largedata set but less value. The feedback instance can be constructed backon only collecting the ninety attributes. When the needs arise, the AISmodule 326 can look into the data set the virtual router contains, theflexibility of the data collection API, and can then instruct ananalytic module to start collection of the additional ten attributes forcertain duration. The AIS module 326 also can connect with any newlyadded analytic algorithm(s) and/or program(s) for the modified feedbackinstance module.

The VAS module 330 can perform one or more verification operationsbefore activating the modified feedback instance 516 in production. Asan example, verification operations can ensure that all actors are inactive state and there are no other conflicting feedback instance(s)running or other policy attribute restrictions (such as time of day,location, and/or the like). The VAS module 330 can perform one or moreactivation operations to activate the modified feedback instance 516 inproduction.

Turning now to FIG. 6, aspects of a method 600 for modifying an existingfeedback instance, such as the original feedback instance 500, in thefeedback instance architecture 100″ will be described, according to anillustrative embodiment. The method 600 will be described with referenceto FIG. 6 and further reference to FIG. 5. The method 600 begins atoperation 602, where the FICUR 116 receives the feedback instancemodification request 504 from the PE 104. The feedback instancemodification request 504 can be generated by the PE 104 to instruct theFICUR 116 to change, adjust, upgrade, or otherwise modify one or moreexisting, active feedback instances, such as the original feedbackinstance 500. For ease of explanation, the feedback instancemodification request 504 will be described herein as instructing theFICUR 116 to modify the original feedback instance 500 to cure one ormore deficiencies of the original feedback instance 500.

From operation 602, the method 600 proceeds to operation 604, where theFICUR 116 examines the feedback instance modification request 504 todetermine which aspects of the original feedback instance 500 are to bemodified. At operation 604, the FICUR 116 also interacts with the PE 104to establish one or more modified objectives to be used to enhance theoriginal feedback instance 500 to cure one or more deficiencies of theoriginal feedback instance 500.

From operation 604, the method 600 proceeds to operation 606, where theFICUR 116 uses a pre-defined extensibility policy to extend thespecification of the original feedback instance model utilized to createthe original feedback instance 500 to allow for the creation of amodified feedback instance model to create the modified feedbackinstance 516. An extensibility policy allows an original feedbackinstance model, used to create an original feedback instance, to beextended in various ways to support creation of a modified feedbackinstance. A feedback instance model might be allowed to be modified onlyif the result will still be within a pre-defined set of extensibilitypolicies or constraints. A network fault management feedback instancemight not be allowed to be extended to become a security virus detectionfeedback instance. A compute-based feedback instance might not beallowed to be extended to a network monitoring feedback instance. When amodification request is received and analyzed, a new model objectivemight be determined that can involve adding or dropping some of theactors in the original feedback instance. The new model objective mightinvolve maintaining the same set of actors but simply adding orremoving/modifying some of the actions that are allowed to be taken, oradding/removing/modifying some attributes from data collection module,or adding/removing/modifying new limitation to the API definitionbetween two actors. The allowed manner and extent of allowablemodification is defined by the extensibility policies. For example, fora VM restart compute feedback instance, the extensibility policies mightbe to allow extending the model to another region but within California(or another state) only, to allow extending the model to include one ormore hypervisors, to allow extending the feedback instance only if theaverage the controller CPU utilization is below 70%, to allow extendingthe feedback instance only if the actors added in belong to othercompute infrastructure within the same data center.

From operation 606, the method 600 proceeds to operation 608, where theFICUR 116 modifies the original feedback instance model in accordancewith the extended specification. From operation 608, the method 600proceeds to operation 610, where the FICUR 116 stores the modifiedfeedback instance model in the active feedback instance model storage142 of the feedback instance model repository 110.

From operation 610, the method 600 proceeds to operation 612, where theFIOC 126 receives the feedback instance modification realization request514, including a model identifier associated with the modified feedbackinstance model, from the FICUR 116. From operation 612, the method 600proceeds to operation 614, where the FIOC 126 retrieves the modifiedfeedback instance model from the feedback instance model repository 110in accordance with the model identifier received in the feedbackinstance modification realization request 514.

From operation 614, the method 600 proceeds to operation 616, where theFIOC 126 performs feedback instance model realization operations todeploy the modified feedback instance 516 in production. From operation616, the method 600 proceeds to operation 618, where the FIOC 126validates the modified feedback instance 516. From operation 618, themethod 600 proceeds to operation 620, where the FIOC 126 monitors themodified feedback instance 516 based upon one or more operationalparameters.

From operation 620, the method 600 proceeds to operation 622. The method600 ends at operation 622.

Turning now to FIG. 7, a block diagram illustrating further aspects of afeedback instance architecture 100′″ will be described, according to anillustrative embodiment. The illustrated feedback instance architecture100′″ includes the policy requestor 102, the PE 104, the policyrepository 106, the FIER 118, the feedback instance model repository110, and the FIOC 126. Each of these components and others will bedescribed in detail below. While connections are shown between some ofthe components illustrated in FIG. 7, it should be understood that some,none, or all of the components illustrated in FIG. 7 can be configuredto interact with one another to carry out various operations describedherein. Thus, it should be understood that FIG. 7 and the followingdescription are intended to provide a general understanding of asuitable environment in which various aspects of embodiments can beimplemented, and should not be construed as being limiting in any way.

The PE 104 can receive the policy request 130 from the policy requestor102 and can determine, through a policy decision process, whether or nota policy exists that can be utilized to satisfy the policy request 130.If so, the PE 104 can activate the policy and can instruct the policyenforcement point(s) 108 to enforce the policy. The illustrated PE 104includes the PE processing unit 300, the PE memory unit 302, and the PEmodule 304 described above. The PE module 304 can be executed by the PEprocessing unit 300 to perform operations in response to the policyrequest 130. More particularly, the PE module 304 can execute operationsof a policy decision process through which the PE module 304 determineswhether to activate one or more existing policies, such as one or moreof the policies 138, stored in the policy repository 106. If the PEmodule 304 determines that at least one of the policies 138 in thepolicy repository 106 should be activated in response to the policyrequest 130, the PE module 304 can retrieve the one or more policies 138from the policy repository 106 and can instruct the policy enforcementpoint(s) 108 to enforce the one or more policies 138. If, however, thePE module 304 determines that none of the policies 138 in the policyrepository 106 should be activated in response to the policy request130, the PE module 304 can determine whether an existing, activefeedback instance, such as the original feedback instance 500, can beutilized to satisfy the policy request 130. If an existing, activefeedback instance can be utilized to satisfy the policy request 130, thePE 104 can associate that existing, active feedback instance with thepolicy request 130. For example, if the PE 104 determines that theoriginal feedback instance 500 can be utilized to satisfy the policyrequest 130, the PE 104 can associate the original feedback instance 500with the policy request 130. If, however, the original feedback instance500 cannot be utilized to satisfy the policy request 130, the PE 104 canidentify one or more deficiencies in the original feedback instance 500that can be cured via an escalation process disclosed herein. Forexample, the original feedback instance 500 might fail to meet one ormore objectives or might detect an anomaly that the original feedbackinstance 500 cannot handle. The escalation process described herein canenable escalation of the issue experienced by the original feedbackinstance 500 to another feedback instance, as will be described infurther detail herein.

The illustrated original feedback instance 500 includes the originalfeedback instance actors 502A-502N, although the original feedbackinstance 500 might include a different number of actors. The originalfeedback instance 500 might be deficient in one or more aspects. Theoriginal feedback instance 500 might be deficient in a number of actorsand/or type of actor. In some embodiments, the original feedbackinstance 500 includes a particular actor but can be deficient in thatthe particular actor is inactive. Alternatively, in other embodiments,the original feedback instance 500 does not include an actor and, inthis manner, is deficient. The original feedback instance 500 might bedeficient in a number of inputs and/or type of inputs. The originalfeedback instance 500 might be deficient in a type or number ofoperational parameters and/or values associated therewith. The originalfeedback instance 500 might be deficient in one or more analyticapplications, and might benefit from or require the addition of one ormore analytic applications and/or the removal of one or more analyticapplications to cure a deficiency. The original feedback instance 500might need changes to other attributes such as, but not limited, tocapacity, traffic, and the like.

The PE 104 can generate a feedback instance escalation request 700directed to the FIER 118 to notify the FIER 118 of one or moredeficiencies in the original feedback instance 500. The FIER 118 canconvey issues (e.g., deficiencies) unresolved in one feedback instanceto another active or non-active feedback instance. The illustrated FIER118 includes an FIER processing unit 702, an FIER memory unit 704, anFIER EIS module 706, and the FIER EDS module 708. The FIER EIS module706 and the FIER EDS module 708 can be stored in the FIER memory unit704 and can be executed by the FIER processing unit 702 to cause theFIER 118 to perform operations to escalate feedback instances inaccordance with embodiments disclosed herein.

The FIER processing unit 702 can be or can include one or more CPUsconfigured with one or more processing cores. The FIER processing unit702 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FIER processing unit 702 can beor can include one or more discrete GPUs. In some other embodiments, theFIER processing unit 702 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFIER processing unit 702 can be or can include one or more FPGAs. TheFIER processing unit 702 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FIER memory unit 704. In some embodiments, the FIERprocessing unit 702 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FIER processing unit 702 can be or can include oneor more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FIER processing unit 702 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FIER processing unit 702can utilize various computation architectures, and as such, the FIERprocessing unit 702 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FIER memory unit 704 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FIER memory unit704 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FIER processing unit 702.

The FIER EIS module 706 can receive and examine feedback instanceescalation requests, such as the illustrated feedback instanceescalation request 700 received from the PE 104. The FIER EIS module 706can receive and examine other feedback instance escalation requests fromother participating actors. Escalation requests might often or usuallycome from the policy engine 104. However, in some cases, an escalationrequest can come from or result from any of the participating actors.For example, one cloud controller might raise concerns regardingconsistently encountering resource contention issues but the impact isconfined within the cloud controller's own domain (i.e., not within thecurrent scope of the feedback instance). The FIER 118 might still takethis request seriously and might decide to escalate to another moreappropriate feedback instance in order to relieve some burden from thelocal cloud controller. The FIER EIS module 706 can provide a feedbackinstance escalation realization request 710 to the FIOC 126 so that theFIER 118 can perform model escalation operations described herein. Thefeedback instance escalation realization request 710 can include asuggested escalation model identifier associated with a suggestedfeedback instance escalation model. The suggested feedback instanceescalation model can be determined by the FIER EDS module 708 asdescribed below.

The FIER EDS module 708 can examine the feedback instance escalationrequest 700 along with the objective(s) of the original feedbackinstance 500 using policy rules to define one or more new modelobjectives. Policy can be used to define new model objectives. Forexample, the original model for a particular feedback instance might beto focus on VM performance within a single data center. However, due tosome problem that has occurred in a nearby data center, many of theresources are now used to support that data center. The objective of theoriginal feedback instance that has been defined to have a local (singledata center) view might no longer be satisfactory. A new objective mightbe to monitor the process across multiple data centers. In this case,the issue might be better addressed by escalating to another feedbackinstance that already has a view into all four of the data centers inquestion.

Based upon the new model objective(s), the FIER EDS module 708 cansearch the non-active feedback instance model storage 140 and the activefeedback instance model storage 142 of the feedback instance modelrepository 110 for possible feedback instance escalation modelcandidates. The FIER EDS module 708 also can calculate a distance scoreamong all or a subset of the possible model candidates. Distance scoresmight be calculated to determine how alike or unlike two feedbackinstances are, both in terms of particular feedback instancedefinitional dimension aspects and also overall. Overall distances canbe calculated, for example, by summing or averaging the distance valuesof each definitional dimension or aspect. Definitional/model aspects caninclude: the various types, extents, specifics, and frequency of datacollected by a feedback instance; the type, degree, and otherdescriptive factors regarding analysis to be performed (and multiples ofthese if multiple types of analysis are included in the feedbackinstance); the number of participating actors and associated specificidentities; the target(s) (which may be actors or particularaspects/components of actors) of the feedback instance in terms of whatparticular entity/behavior/functionality orentities/behaviors/functionalities the feedback instance is intended toaffect; and any other possible feedback instance definition componentsthat may exist or may be added. For each separable aspect, or for groupsof aspects in any reasonable combination, distance values might beconfigured such that certain values or settings of aspect(s) are closertogether or farther apart (i.e., having shorter/smaller orlarger/farther “distances” therebetween). Actual values can be numericalor in any other form or format that might be useful. For example, when afeedback instance escalation request is received, in many cases, morethan one feedback instance may be suitable as the escalation target. Inorder to determine to which feedback instance to escalate, a distancescore might help in determining which feedback instance to select (e.g.,the feedback instance that is closest—shortest overall “distance”- towhat is needed).

The FIER EDS module 708 can select a best match based upon which of thepossible model candidates has the highest distance score. The model withthe highest distance score can be determined to be the suggestedfeedback instance escalation model. The FIER EDS module 708 can providea suggested model escalation identifier associated with the suggestedfeedback instance escalation model to the FIER EIS module 706, which cangenerate the feedback instance escalation realization request 710including the suggested model escalation identifier, and can provide thefeedback instance escalation realization request 710 to the FIOC 126.

The FIER 118, in some embodiments, is implemented as a policyapplication executable by the PE processing unit 300 of the PE 104. Inother embodiments, such as the illustrated embodiment, the FIER 118 isimplemented as an external entity that is in communication with the PE104 to support the escalation of feedback instance models. A feedbackinstance escalation request, such as the feedback instance escalationrequest 700, can be received from the PE 104, although in someembodiments, the FIER 118 can subscribe to requests directly from anactor external to the PE 104.

The illustrated FIOC 126 is located downstream from the FIER 118 in thefeedback instance architecture 100. The FIOC 126 can respond to thefeedback instance escalation realization request 710 received from theFIER 118 to realize—that is, to put into production—the feedbackinstance escalation model identified via a model identifier in thefeedback instance escalation realization request 710. The FIOC 126 canfunction as a supporting entity to the FIER 118. In some embodiments,the functionality of the FIOC 126 is combined with the functionality ofthe FIER 118. In other embodiments, such as the illustrated embodiment,the FIOC 126 is implemented as an external entity that is incommunication with the FIER 118.

The illustrated FIOC 126 includes the FIOC processing unit 322, the FIOCmemory unit 324, a feedback instance reactivation subsystem (“FIRS”)module 712, and a reactivation and association subsystem (“RAS”) module714. The FIRS module 712 and the RAS module 714 can be stored in theFIOC memory unit 324 and can be executed by the FIOC processing unit 322to cause the FIOC 126 to perform escalation operations described herein.

The FIRS module 712 can receive the feedback instance escalationrealization request 710 from the FIER 118. The FIRS module 712 can beexecuted when the model identifier received in the feedback instanceescalation realization request 710 is related to a non-active feedbackinstance. When the non-active feedback instance model is located, theFIRS module 712 can validate the non-active feedback instance to ensurethe non-active feedback instance model meets the entire specificationfor the non-active feedback instance as stored in the non-activefeedback instance model storage 140 of the feedback instance modelrepository 110.

The RAS module 714 can reactivate the validated, non-active feedbackinstance and can associate the feedback escalation realization request710 with the reactivated feedback instance, which, in the illustratedexample, is an escalated feedback instance 716 that includes one or moreescalated feedback instance actors 718A-718N. The RAS module 714 canenable one or more visualization policies so that the escalated feedbackinstance can be visually observed by operations pertaining to theescalation identifier. Visualization policies can allow an escalatedfeedback instance to be visually observed by operations pertaining to anescalation identifier in various ways, including any filters to beapplied, preferred visualization mechanisms or factors, preferences orrequirements applying, conditional aspects, and the like. For instance,once a feedback instance is escalated to a different feedback instance,the operations personnel should be able to visualize the newly-escalatedfeedback instance. Operations personnel might benefit from visualindications that escalation has occurred, the scope or purpose of theescalation, the cause of the escalation, the progress or current statusregarding the escalation objective, any associated constraints orlimitations, any conditions that might result in a request for manualintervention, and the like. Which of these apply, how they apply, and/ormechanisms to be used can be included in visualization policies.

The FIOC 126 can be implemented as an orchestration application within aservice and/or resource orchestrator or can be implemented as anexternal entity in communication with such an orchestrator. When theFIOC 126 is implemented as an external entity to the orchestrator, theFIOC 126 can leverage the existing interface infrastructure of theorchestrator.

Based on the foregoing, an escalation can include a determination that asecond feedback instance is to be assigned an additional responsibilityand an assignment of the additional responsibility to the secondfeedback instance based upon information provided by the FIER 118. TheFIER 118 can adjust the definition of the second feedback instance tohave an expanded or multiple scopes.

Turning now to FIG. 8, aspects of a method 800 for escalating a feedbackinstance, such as the original feedback instance 500, in the feedbackinstance architecture 100′″ will be described, according to anillustrative embodiment. The method 800 will be described with referenceto FIG. 8 and further reference to FIG. 7. The method 800 begins atoperation 802, where the FIER 118 receives the feedback instanceescalation request 700 from the policy engine 104. From operation 802,the method 800 proceeds to operation 804, where the FIER 118 examinesthe feedback instance escalation request 700 and one or more objectivesof the original feedback instance 500 to identify one or more escalatedobjectives for the escalated feedback instance 716.

From operation 804, the method 800 proceeds to operation 806, where theFIER 118 creates a definition for the escalated feedback instance 716.The definition can include the escalated objective(s) identified inoperation 804 along with one or more escalated targets and one or moreescalated inputs to satisfy the escalated objective(s). The escalatedtarget(s) can include one or more actors to be influenced by theescalated feedback instance 716. The escalated input(s) can include thespecific input(s) to be utilized by the escalated feedback instance 716.

From operation 806, the method 800 proceeds to operation 808, where theFIER 118 maps the definition for the escalated feedback instance 716 toone or more active feedback instance models identified in the activefeedback instance model storage 142 of the feedback instance modelrepository 110 and/or to one or more non-active feedback instance modelsidentified in the non-active feedback instance model storage 140 of thefeedback instance model repository 110. In some embodiments, the FIER118 can utilize a table of all or a relevant subset of available activeand non-active feedback instances to map the definition for theescalated feedback instance 716. The table can be updated to accommodatenew or different feedback instance models.

From operation 808, the method 800 proceeds to operation 810, where theFIER 118 calculates a distance score for each qualified feedbackinstance model, or in other words, to each feedback instance model towhich the definition for the escalated feedback instance 716 has beenmapped in operation 808. From operation 810, the method 800 proceeds tooperation 812, where the FIER 118 selects the closest match based uponthe distance scores calculated at operation 810. Although not shown inthe illustrated example, a feedback instance model that is the closestmatch can be selected as a candidate for the escalated feedback instance716 if the distance between the closest match and the definition for theescalated feedback instance 716 does not exceed one of more thresholds.

Thresholds might be used for many purposes, for example, in thedetermination of whether the “distance” between the closest match andthe definition for the escalated feedback instance does not exceed oneof more settable values along various dimensions. Multiple thresholdscan be set, each for an acceptable maximum distance along a particulardimension. Dimensions might be defined for each aspect of the feedbackinstance definition, such that these dimensions or scales denote howclosely two feedback instances are alike. The greater the “distances”between values or settings in dimensions, the more the feedbackinstances are different. Whereas the shorter or smaller the distancesbetween them, the more the feedback distances are alike. If alldistances are zero, reflecting no difference in the definitional aspectsof the feedback instances, then two feedback instances are identical.Also, when looking for a feedback instance to which to escalate, onemight find out that although there are a set of feedback instances towhich to escalate, none of the feedback instances may match to theobjectives 100%. One approach to help resolve this is to set a thresholdrule. For example, if less than 70% of objectives can be handled by anidentified feedback instance for escalation, then this percentage mightbe deemed unacceptable (i.e., not good enough), such that a brand newfeedback instance will instead have to be created to which to escalate.If the distance does exceed one or more thresholds, the FIER 118 caninstruct the FIOC 126 to create a new feedback instance model to resolvethe issue(s) that prompted the escalation process.

From operation 812, the method 800 proceeds to operation 814, where theFIER 118 generates the feedback instance escalation realization request710. From operation 814, the method 800 proceeds to operation 816, wherethe FIER 118 sends the feedback instance escalation realization request710 to the FIOC 126. From operation 816, the method 800 proceeds tooperation 818, where the FIOC 126 receives the feedback instanceescalation realization request 710 from the FIER 118.

From operation 818, the method 800 proceeds to operation 820, where theFIOC 126 determines whether to activate or reactivate a non-active modelor to associate the feedback instance escalation realization request 710with an active feedback instance. From operation 820, the method 800proceeds to operation 822, where if the FIOC 126 has determined toactivate or re-activate a non-active feedback instance model, the method800 proceeds to operation 824, where the FIOC 126 retrieves a non-activefeedback instance model and activates the non-active feedback instancemodel in runtime as the escalated feedback instance 716. From operation824, the method 800 proceeds to operation 826, where the method 800ends. If, however, at operation 822, the FIOC 126 has determined not toactivate or re-activate a non-active feedback instance model, the method800 proceeds to operation 828, where the FIOC 126 associates thefeedback instance escalation realization request 710 with an activefeedback instance in runtime as the escalated feedback instance 716.From operation 824, the method 800 proceeds to operation 826, where themethod 800 ends.

Turning now to FIG. 9, a block diagram illustrating further aspects ofthe feedback instance architecture 100″″ will be described, according toan illustrative embodiment. The illustrated feedback instancearchitecture 100″″ includes the policy requestor 102, the PE 104, thepolicy repository 106, the FICIR 120, a feedback instance modelrepository 110, and the FIOC 126. Each of these components and otherswill be described in detail below. While connections are shown betweensome of the components illustrated in FIG. 9, it should be understoodthat some, none, or all of the components illustrated in FIG. 9 can beconfigured to interact with one another to carry out various operationsdescribed herein. Thus, it should be understood that FIG. 9 and thefollowing description are intended to provide a general understanding ofa suitable environment in which various aspects of embodiments can beimplemented, and should not be construed as being limiting in any way.

The PE 104 can receive the policy request 130 from the policy requestor102 and can determine, through a policy decision process, whether or nota policy exists that can be utilized to satisfy the policy request 130.If so, the PE 104 can activate the policy and can instruct the policyenforcement point(s) 108 to enforce the policy. The illustrated PE 104includes the PE processing unit 300, the PE memory unit 302, and the PEmodule 304.

The PE module 304 can be executed by the PE processing unit 300 toperform operations in response to the policy request 130. Moreparticularly, the PE module 304 can execute operations of a policydecision process through which the PE module 304 determines whether toactivate one or more existing policies, such as one or more of thepolicies 138, stored in the policy repository 106. If the PE module 304determines that at least one of the policies 138 in the policyrepository 106 should be activated in response to the policy request130, the PE module 304 can retrieve one or more of the policies 138 fromthe policy repository 106 and can instruct the policy enforcementpoint(s) 108 to enforce one or more of the policies 138. If, however,the PE module 304 determines that none of the policies 138 in the policyrepository 106 should be activated in response to the policy request130, the PE module 304 can determine whether an existing, activefeedback instance, such as the original feedback instance 500, can beutilized to satisfy the policy request 130. If an existing, activefeedback instance can be utilized to satisfy the policy request 130, thePE 104 can associate that existing, active feedback instance with thepolicy request 130. For example, if the PE 104 determines that theoriginal feedback instance 500 can be utilized to satisfy the policyrequest 130, the PE 104 can associate the original feedback instance 500with the policy request 130. If, however, the original feedback instance500 cannot be utilized to satisfy the policy request 130, the PE 104 canidentify one or more deficiencies in the original feedback instance 500that can be cured via a feedback instance consultation process disclosedherein.

The illustrated original feedback instance 500 includes the OFI actors502A-502N, although the original feedback instance 500 might include adifferent number of actors. The original feedback instance 500 might bedeficient in one or more aspects. The original feedback instance 500might be deficient in a number of actors and/or type of actor. In someembodiments, the original feedback instance 500 includes a particularactor but can be deficient in that the particular actor is inactive.Alternatively, in other embodiments, the original feedback instance 500does not include an actor and, in this manner, is deficient. Theoriginal feedback instance 500 might be deficient in a number of inputsand/or type of inputs. The original feedback instance 500 might bedeficient in a type or number of operational parameters and/or valuesassociated therewith. The original feedback instance 500 might bedeficient in one or more analytic applications, and might benefit fromor require the addition of one or more analytic applications and/or theremoval of one or more analytic applications to cure a deficiency. Theoriginal feedback instance 500 might need changes to other attributessuch as, but not limited, to capacity, traffic, and the like.

In response to identifying one or more deficiencies, the originalfeedback instance 500 can generate a consultation request 900 directedto the policy engine 104. The consultation request 900 can be utilizedby the original feedback instance 500 to request help from one or moredifferent feedback instances, such as a target feedback instance 902,which can include one or more actors, such as target feedback instanceactor(s) 904A-904N, that can utilize a consultation communications link906 to communicate with the original feedback instance actor(s)502A-502N to help the original feedback instance 500 cure one or moredeficiencies.

In response to receiving the consultation request 900 from the originalfeedback instance 500, the PE 104 can generate a feedback instanceconsultation request 908. The PE 104, in some embodiments, can generatethe feedback instance consultation request 908 after verifying thevalidity of the consultation request 900. For example, the PE 104 canreference one or more policies regarding permissions for the originalfeedback instance 500 to consult with one or more different feedbackinstances, such as the target feedback instance 902. Alternatively, thePE 104, in some embodiments, can function as a pass-through forcommunications between the original feedback instance 500 and the FICIR120. In these embodiments, the feedback instance consultation request908 might be the same as or similar to the consultation request 900. Ineither the case, the FICIR 120 can receive the feedback instanceconsultation request 908 and can perform one or more operations of aconsultation process described in further detail herein below, at leastin part, to help the original feedback instance 500 cure one or moredeficiencies.

The illustrated FICIR 120 includes an FICIR processing unit 910, anFICIR memory unit 912, an FICIR EIS module 914, an API method subsystem(“APIMS”) module 916, and an event/response method subsystem (“ERMS”)module 918. The FICIR EIS module 914, the APIMS module 916, and the ERMSmodule 918 can be stored in the FICIR memory unit 912 and can beexecuted by the FICIR processing unit 910 to cause the FICIR 120 toperform operations to facilitate consultation among feedback instancesin accordance with embodiments disclosed herein.

The FICIR processing unit 910 can be or can include one or more CPUsconfigured with one or more processing cores. The FICIR processing unit910 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FICIR processing unit 910 can beor can include one or more discrete GPUs. In some other embodiments, theFICIR processing unit 910 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFICIR processing unit 910 can be or can include one or more FPGAs. TheFICIR processing unit 910 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FICIR memory unit 912. In some embodiments, the FICIRprocessing unit 910 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FICIR processing unit 910 can be or can includeone or more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FICIR processing unit 910 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FICIR processing unit 910can utilize various computation architectures, and as such, the FICIRprocessing unit 910 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FICIR memory unit 912 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FICIR memory unit912 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FICIR processing unit 910.

The FICIR EIS module 914 can receive and examine consultation requests,such as the illustrated feedback instance consultation request 908received from the PE 104. The FICIR EIS module 914 can deliver to theFIOC 126 a feedback instance consultation realization request 920. Thefeedback instance consultation realization request 920 can include arequesting model identifier associated with a requesting feedbackinstance model such as the model associated with the original feedbackinstance 500. The feedback instance consultation realization request 920also can include a target model identifier associated with a targetfeedback instance model, such as the model associated with the targetfeedback instance 902.

The APIMS module 916 can examine the feedback instance consultationrequest 908 and can search an active feedback instance model API library922 stored in the feedback instance model repository 110 for anavailable API exposed by (i.e., offered by) a peer feedback instance,such as the target feedback instance 902 in the illustrated example, toallow consultation communications via the consultation communicationslink 906. The active feedback instance model API library 922 can includeone or more APIs for each active feedback instance model in the activefeedback instance model storage 142 upon which one or more currentlyactive feedback instances are based (i.e., feedback instance(s)operating in runtime).

The ERMS module 918 can map the feedback instance consultation request908 to an event/response already supported by a peer feedback instance,such as the target feedback instance 902. The result of one or moreoperations performed by the APIMS module 916 and the ERMS module 918upon execution by the FICIR processing unit 910 can be delivered to theFIOC 126 via the FICIR EIS module 914 in the feedback instanceconsultation realization request 920 to establish the consultationcommunications link 906 between the original feedback instance 500 andthe target feedback instance 902.

The FICIR 120, in some embodiments, is implemented as a policyapplication executable by the PE processing unit 300 of the PE 104. Inother embodiments, such as the illustrated embodiment, the FICIR 120 isimplemented as an external entity that is in communication with the PE104 to support consultation among active feedback instances. A feedbackinstance consultation request, such as the illustrated feedback instanceconsultation request 908, can be received from the PE 104, although insome embodiments, the FICIR 120 can subscribe to requests directly froman actor external to the PE 104.

The illustrated FIOC 126 is located downstream from the FICIR 120 in thefeedback instance architecture 100. The FIOC 126 can respond to thefeedback instance consultation realization request 920 received from theFICIR EIS module 914 to realize—that is, to put into production—theconsultation communications link 906 between the instances identified inthe feedback instance consultation realization request 920, such as theoriginal feedback instance 500 and the target feedback instance 902 inthe illustrated example. The FIOC 126 can function as a supportingentity to the FICIR 120. In some embodiments, the functionality of theFIOC 126 is combined with the functionality of the FICIR 120. In otherembodiments, such as the illustrated embodiment, the FIOC 126 isimplemented as an external entity that is in communication with theFICIR 120.

In response to the feedback instance consultation realization request920, the FIOC 126 can create and/or extend one or moreintercommunication paths among two or more feedback instances. In theillustrated example the FIOC 126 creates the consultation communicationslink 906 between the original feedback instance 500 and the targetfeedback instance 902. In some embodiments, the FIOC 126 can extend theconsultation communications link 906 to facilitate communications withone or more other feedback instances.

The illustrated FIOC 126 includes the FIOC processing unit 322, the FIOCmemory unit 324, a feedback instance API interaction subsystem(“FIAPIIS”) module 924, and a feedback instance event/responseinteraction subsystem (“FIERIS”) module 926. The FIAPIIS module 924 andthe FIERIS module 926 can be stored in the FIOC memory unit 324 and canbe executed by the FIOC processing unit 322 to cause the FIOC 126 toperform consultation realization operations described herein.

The FIAPIIS module 924 can receive the feedback instance consultationrealization request 920 from the FICIR 120. The FIAPIIS module 924 canbe executed by the FIOC processing unit 322 to invoke an API call to oneor more APIs published in the active feedback instance model API library922 to establish communication with one or more target feedbackinstances, such as the illustrated target feedback instance 902. TheFIERIS module 926 can be executed by the FIOC processing unit 322 toenable the requesting feedback instance, such as the original feedbackinstance 500, to execute operations of an event/response method tocommunicate with the target feedback instance, such as the targetfeedback instance 902, for consultation purposes to satisfy, at least inpart, the policy request 130.

Turning now to FIG. 10, aspects of a method 1000 for consulting amongfeedback instances in the feedback instance architecture 100″″ will bedescribed, according to an illustrative embodiment. The method 1000 willbe described with reference to FIG. 10 and further reference to FIG. 9.The method 1000 begins at operation 1002, where the original feedbackinstance 500 generates the consultation request 900 directed to the PE104 to request establishment of a feedback instance intercommunicationlink, such as the consultation communications link 906, over which toreceive assistance from one or more target feedback instances, such asthe target feedback instance 902 in the example illustrated in FIG. 9,to help the original feedback instance 500 cure one or more deficienciesin a feedback instance model upon which the original feedback instance500 is based. From operation 1002, the method 1000 proceeds to operation1004, where the original feedback instance 500 sends the consultationrequest 900 to the PE 104.

From operation 1004, the method 1000 proceeds to operation 1006, wherethe PE 104 receives the consultation request 900 from the originalfeedback instance 500. From operation 1006, the method proceeds tooperation 1008, where the PE 104 examines and verifies the consultationrequest 900 to generate the feedback instance consultation request 908.From operation 1008, the method 1000 proceeds to operation 1010, wherethe PE 104 sends the feedback instance consultation request 908 to theFICIR 120. The PE 104, in some embodiments, can generate the feedbackinstance consultation request 908 after verifying the validity of theconsultation request 900. For example, the PE 104 can reference one ormore policies regarding permissions for the original feedback instance500 to consult with one or more different feedback instances, such asthe target feedback instance 902. Alternatively, the PE 104, in someembodiments, can function as a pass-through for communications betweenthe original feedback instance 500 and the FICIR 120. In theseembodiments, the feedback instance consultation request 908 might be thesame as or similar to the consultation request 900.

From operation 1010, the method 1000 proceeds to operation 1012, wherethe FICIR 120 examines the feedback instance consultation request 908 todetermine if a match exists with one or more APIs associated with thetarget feedback instance model upon which the target feedback instance902 is based. If, at operation 1014, the FICIR 120 determines that amatch does not exist, the method 1000 proceeds to operation 1016. Atoperation 1016, the FICIR 120 maps the feedback instance consultationrequest 908 to one or more objectives of a target feedback instancemodel available from the active feedback instance model storage 142 ofthe feedback instance model repository 110 that can be utilized to cureone or more deficiencies of the original feedback instance 500.

From operation 1016, the method 1000 proceeds to operation 1018, wherethe FICIR 120 maps the feedback instance consultation request 908 to anevent/response pair that is supported by the target feedback instancemodel to which the feedback instance consultation request 908 was mappedat operation 1016. The event/response pair can include an event to whichthe target feedback instance model subscribes. The event/response paircan include an event response to which the original feedback instancemodel should subscribe.

From operation 1018, the method 1000 proceeds to operation 1020, wherethe FICIR 120 updates the original and target feedback instance modelswith an intercommunication plan via an API and/or an event. Also, if, atoperation 1014, the FICIR 120 determines that a match does exist, themethod 1000 proceeds directly to operation 1020.

From operation 1020, the method 1000 proceeds to operation 1022, wherethe FICIR 120 directs the FIOC 126 to realize a communication methodbetween the original and target feedback instance models via thefeedback instance consultation realization request 920. From operation1022, the method 1000 proceeds to operation 1024, where the FIOC 126retrieves the modified (i.e., updated) original and target feedbackinstance models from the active feedback instance model storage 142 ofthe feedback instance model repository 110. From operation 1024, themethod 1000 proceeds to operation 1026, where the FIOC 126 enablescommunication (e.g., establishes the consultation communications link906) or enhances communication (e.g., extends connectivity of theconsultation communications link 906) between the original and targetfeedback instance models, upon which the original feedback instance 500and the target feedback instance 902 are respectively based.

From operation 1026, the method 1000 proceeds to operation 1028. Themethod 1000 ends at operation 1028.

Turning now to FIG. 11, a block diagram illustrating further aspects ofthe feedback instance architecture 100′″″ will be described, accordingto an illustrative embodiment. The illustrated feedback instancearchitecture 100′″″ includes the PE 104, the policy repository 106, theFICOR 122, the feedback instance model repository 110, and the FIOC 126.Each of these components and others will be described in detail below.While connections are shown between some of the components illustratedin FIG. 11, it should be understood that some, none, or all of thecomponents illustrated in FIG. 11 can be configured to interact with oneanother to carry out various operations described herein. Thus, itshould be understood that FIG. 11 and the following description areintended to provide a general understanding of a suitable environment inwhich various aspects of embodiments can be implemented, and should notbe construed as being limiting in any way.

In the illustrated embodiment, a feedback instance group 1100 includes aplurality of feedback instances 128A, 128B, 128N, each of which includesone or more actors 1102A-1102N, 1104A-1104N, 1106A-1106N, respectively.The feedback instance group 1100 can generate one or more feedbackinstance events 1108. Each of the feedback instance events 1108 can begenerated by one of the plurality of feedback instances 128A, 128B, 128Nin the feedback instance group 1100 in response to detection of one ormore anomalies. In some cases, a single set of anomalies might not beenough for the PE 104 or other decision making system to execute acomplete optimization plan. The feedback instance events 1108 from thefeedback instance group 1100 might need, or at least might benefit insome way from, being coordinated to derive an optimization plan. Inorder to support dynamic coordination, the FICOR 122 and the FIOC 126will be utilized.

The PE 104 can receive the feedback instance events 1108 (or data suchas the event data 132 associated therewith, as illustrated in FIG. 1).In the illustrated embodiment, the PE 104 can pass the feedback instanceevents 1108 to the FICOR 122. In other embodiments, the FICOR 122 cansubscribe to the feedback instance group 1100 to receive all or aportion of the feedback events 1108. In other embodiments, the FICOR 122can create the feedback instance group 1100 and can subscribe to two ormore events generated by or otherwise associated with the feedbackinstance group 1100. The FICOR 122 can receive the feedback instanceevents 1108 and can leverage one or more policies, such as one or moreof the policies 138, retrieved from the policy repository 106 to developa coordinated optimization plan to optimize interactions among thefeedback instances 128A-128N in the feedback instance group.

The FICOR 122 includes a FICOR processing unit 1110, a FICOR memory unit1112, a FICOR EIS module 1114, a method extender subsystem (“MES”)module 1116, and an optimization resolver subsystem (“ORS”) module 1118.The FICOR EIS module 1114, the MES module 1116, and the ORS module 1118can be stored in the FICOR memory unit 1112 and can be executed by theFICOR processing unit 1110 to cause the FICOR 122 to perform operationsto coordinate and optimize events among feedback instances in a feedbackinstance group in accordance with embodiments disclosed herein.

The FICOR processing unit 1110 can be or can include one or more CPUsconfigured with one or more processing cores. The FICOR processing unit1110 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FICOR processing unit 1110 can beor can include one or more discrete GPUs. In some other embodiments, theFICOR processing unit 1110 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFICOR processing unit 1110 can be or can include one or more FPGAs. TheFICOR processing unit 1110 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FICOR memory unit 1112. In some embodiments, the FICORprocessing unit 1110 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FICOR processing unit 1110 can be or can includeone or more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FICOR processing unit 1110 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of FICOR processing unit 1110 canutilize various computation architectures, and as such, FICOR processingunit 1110 should not be construed as being limited to any particularcomputation architecture or combination of computation architectures,including those explicitly disclosed herein.

The FICOR memory unit 1112 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FICOR memory unit1112 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FICIR processing unit 910.

The FICOR EIS module 1114 can receive and examine the feedback instanceevents 1108 received from the feedback instance group 1100 that shouldbe coordinated. The FICOR EIS module 1114 also can provide one or moremodel extension requests 1120 to the FIOC 126 to instruct the FIOC 126to extend a selected set of feedback instances and deliver a finalizedoptimization plan to the FIOC 126 for execution.

The MES module 1116 can determine whether feedback instance model(s) inthe selected set of feedback instances should be extended. If the MESmodule 1116 determines that feedback instance model(s) in the selectedset of feedback instances should be extended, the MES module 1116 caninstruct the FIOC 126 to extend the feedback instance model(s) via oneor more model extension requests 1120. The ORS module 1118 can map thefeedback instance events 1108 collected from the feedback instance group1100 to a coordinated optimization plan and can instruct the FIOC 126 torealize the coordinated optimization plan via a realization request1122.

The FICOR 122, in some embodiments, is implemented as a policyapplication executable by the PE processing unit 300 of the PE 104. Inother embodiments, such as the illustrated embodiment, the FICOR 122 isimplemented as an external entity that is in communication with the PE104.

The illustrated FIOC 126 is located downstream from the FICOR 122 in thefeedback instance architecture 100. The FIOC 126 can respond to themodel extension request 1120 received from the FICOR EIS module 1114 byextending one or more feedback instance models. The FIOC 126 can respondto the realization request 1122 received from the FICOR EIS module 1114to realize—that is, to put into production—the optimization plan.

The FIOC 126 can function as a supporting entity to the FICOR 122. Insome embodiments, the functionality of the FIOC 126 is combined with thefunctionality of the FICOR 122. In other embodiments, such as theillustrated embodiment, the FIOC 126 is implemented as an externalentity that is in communication with the FICOR 122.

The illustrated FIOC 126 includes the FIOC processing unit 322, the FIOCmemory unit 324, a feedback instance extension subsystem (“FIES”) module1124, and a coordinated optimization orchestration subsystem (“COOS”)module 1126. The FIES module 1124 and the COOS module 1126 can be storedin the FIOC memory unit 324 and can be executed by the FIOC processingunit 322 to cause the FIOC 126 to perform optimization orchestrationoperations described herein.

The FIES module 1124 can receive the model extension request 1120 fromthe FICOR 122. The FIES module 1124 can be executed by the FIOCprocessing unit 322 to extend the event scope of the selected set offeedback instances. The COOS module 1126 is used to enable the executionof the optimization plan by interacting with all or at least a portionof the participating feedback instances. The FIOC 126 can be implementedas an orchestration application within a service/resource orchestratoror can be implemented as an external entity in communication with theorchestrator. When the FIOC 126 is implemented as an external entity tothe orchestrator, the FIOC 126 should attempt to leverage the existinginterface infrastructure of the orchestrator.

Turning now to FIG. 12, a method 1200 for coordinating multiple feedbackinstances to determine optimal actions to be taken in the feedbackinstance architecture 100′″″ will be described, according to anillustrative embodiment. The method 1200 begins and proceeds tooperation 1202, where feedback instances, such as one or more of thefeedback instances 128A-128N send the feedback instance events 1108 tothe FICOR 122. From operation 1202, the method 1200 proceeds tooperation 1204, where the FICOR 122 receives the feedback instanceevents 1108.

From operation 1204, the method 1200 proceeds to operation 1206, wherethe FICOR 122 verifies that all related feedback instances have beenreceived for an optimization plan to optimize actions to be taken by agroup of feedback instances, such as the feedback instance group 1100.From operation 1206, the method 1200 proceeds to operation 1208, wherethe FICOR 122 determines whether events have been received from allrelated feedback instances in the feedback instance group 1100. If not,the method 1200 proceeds to operation 1210, where the FICOR 122continues to wait for events from one or more other related feedbackinstances and the method 1200 returns to operation 1206. After allevents from the feedback instance group 1100 have been received, themethod 1200 proceeds to operation 1212, where the FICOR 122 examines thefeedback instance events 1108 from all feedback instances in thefeedback instance group 1100 to determine whether the feedback instanceevents 1108 can be matched to a single actionable optimization plan.

If, at operation 1214, the FICOR 122 determines that a match does notexist, the method 1200 proceeds from operation 1214 to operation 1216,where the FICOR 122 examines and identifies extensibility of one or morefeedback instances that might provide additional information responsiveto one or more of the feedback instance events 1108. From operation1216, the method 1200 proceeds to operation 1218, where the FICOR 122updates feedback instance model(s) associated with the feedbackinstances from which the feedback instance events 1108 were received inthe active feedback instance model storage 142 of the feedback instancemodel repository 110. From operation 1218, the method 1200 proceeds tooperation 1220, where the FICOR 122 instructs the FIOC 126 to extend thescope, via the model extension request 1120, of one or more feedbackinstances identified at operation 1216. From operation 1220, the method1200 proceeds to operation 1222, where the FIOC 126 retrieves theupdated scope for each feedback instance identified at operation 1216and makes one or more adjustments to each feedback instance in runtime.From operation 1222, the method 1200 proceeds to operation 1224, wherethe extended feedback instances can generate one or more new events. Themethod 1200 can return to operation 1206, where the where the FICOR 122continues to verify that all related feedback instances have beenreceived for the optimization plan. As such, the method 1200, or atleast the aforementioned operations of the method 1200, can be dynamicand continuous, and thus may repeat as new events are obtained by theFICOR 122, and as new optimization plans are sent to the FIOC 126 to beexecuted/enforced by the set of coordinated feedback instances asappropriate.

If, however, at operation 1214, the FICOR 122 determines that a matchexists, the method 1200 proceeds to operation 1226, where the FICOR 122instructs the FIOC 126, via the realization request 1122, to realize thematched optimization plan. From operation 1226, the method 1200 proceedsto operation 1228, where the FIOC 126 communicates an action plan toeach participating feedback instance to execute the optimization plan.From operation 1228, the method 1200 proceeds to operation 1230, wherethe method 1200 ends.

Turning now to FIG. 13, a block diagram illustrating further aspects ofthe feedback instance architecture 100″″″ will be described, accordingto an illustrative embodiment. The illustrated feedback instancearchitecture 100″″″ includes the PE 104; the FIOM/FIMOS 124, whichincludes a FIOM 1300 and a FIMOS 1302; the feedback instance modelrepository 110; the FIOC 126; an active feedback instance performancethreshold policy (“AFIPTP”) repository 1306; and an events/stats andfeedback instance performance repository 1308. Each of these componentsand others will be described in detail below. While connections areshown between some of the components illustrated in FIG. 13, it shouldbe understood that some, none, or all of the components illustrated inFIG. 13 can be configured to interact with one another to carry outvarious operations described herein. Thus, it should be understood thatFIG. 13 and the following description are intended to provide a generalunderstanding of a suitable environment in which various aspects ofembodiments can be implemented, and should not be construed as beinglimiting in any way.

The PE 104 can communicate with the FIOM 1300 and the FIMOS 1302. The PE104 includes the PE processing unit 300, the PE memory unit 302, and thePE module 304 described above. The FIOM 1300 and the FIMOS 1302 cancoordinate adjustment and execution of policy participation level mode(“PPL mode”) based upon current feedback instance conditions. Based ontraffic conditions, computing or network resource conditions, energyconditions and priority considerations, explicit involvement of policycan be automatically or manually tuned/adjusted to a suitable mode. A“mode,” as used herein, can represent a degree or level of guidance,constraints, required interactions, and/or the like provided by apolicy. Alternately, or more generally, modes may reflect different(alternative, in any respect) policy guidance, constraints, requiredinteractions, and/or the like.

The FIOM 1300 includes an FIOM processing unit 1310, an FIOM memory unit1312, and an FIOM module 1314. The FIOM module 1314 can be stored in theFIOM memory unit 1312 and can be executed by the FIOM processing unit1310 to cause the FIOM 1300 to perform operations to monitor feedbackinstances in accordance with embodiments disclosed herein.

The FIOM processing unit 1310 can be or can include one or more CPUsconfigured with one or more processing cores. The FIOM processing unit1310 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FIOM processing unit 1310 can beor can include one or more discrete GPUs. In some other embodiments, theFIOM processing unit 1310 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFIOM processing unit 1310 can be or can include one or more FPGAs. TheFIOM processing unit 1310 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FIOM memory unit 1312. In some embodiments, the FIOMprocessing unit 1310 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FIOM processing unit 1310 can be or can includeone or more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FIOM processing unit 1310 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FIOM processing unit 1310can utilize various computation architectures, and as such, the FIOMprocessing unit 1310 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FIOM memory unit 1312 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FIOM memory unit1312 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FIOM processing unit 1310.

The FIOM 1300, in some embodiments, is implemented as a policyapplication executable by the PE processing unit 300 of the PE 104. Inother embodiments, such as the illustrated embodiment, the FIOM 1300 isimplemented as an external entity that is in communication with the PE104.

The FIOM module 1314 can be executed by the FIOM processing unit 1310 tomonitor performance of one or more active feedback instances, such asthe feedback instance 128, to detect a possible trigger point for a PPLmode adjustment for one or more actors operating as part of the feedbackinstance 128.

The FIMOS 1302 includes a FIMOS processing unit 1316, a FIMOS memoryunit 1318, a FIMOS EIS module 1320, and a participation level resolver(“PLR”) module 1322. The FIMOS EIS module 1320 and the PLR module 1322can be stored in the FIMOS memory unit 1318 and can be executed by theFIMOS processing unit 1316 to cause the FIMOS 1302 to perform operationsto adjust a participation level of one or more actors in one or morefeedback instances, such as the feedback instance 128, in accordancewith embodiments disclosed herein.

The FIMOS processing unit 1316 can be or can include one or more CPUsconfigured with one or more processing cores. The FIMOS processing unit1316 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FIMOS processing unit 1316 can beor can include one or more discrete GPUs. In some other embodiments, theFIMOS processing unit 1316 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFIMOS processing unit 1316 can be or can include one or more FPGAs. TheFIMOS processing unit 1316 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FIMOS memory unit 1318. In some embodiments, the FIMOSprocessing unit 1316 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FIMOS processing unit 1316 can be or can includeone or more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FIMOS processing unit 1316 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FIMOS processing unit 1316can utilize various computation architectures, and as such, the FIMOSprocessing unit 1316 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FIMOS memory unit 1318 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FIMOS memory unit1318 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FIMOS processing unit 1316.

The FIMOS 1302, in some embodiments, is implemented as a policyapplication executable by the PE processing unit 300 of the PE 104. Inother embodiments, such as the illustrated embodiment, the FIMOS 1302 isimplemented as an external entity that is in communication with the PE104.

The FIMOS EIS module 1320 and the PLR module 1322 can be executed by theFIMOS processing unit 1316. The FIMOS EIS module 1320 can receive, fromthe FIOM 1300, one or more events associated with the feedback instance128. The PLR module 1322 can determine whether the policy participationlevel of the feedback instance 128 should be adjusted. The PLR module1322 can then map out the level of participation for each actors infeedback instance 128. The PLR module 1322 also can save changes to theparticipation level in association with the feedback instance model uponwhich the feedback instance 128 is based in the active feedback instancemodel storage 142. The PLR module 1322 also can send a policyparticipation level adjustment request to the FIMOS EIS module 1320 fordelivery to the FIOC 126.

The illustrated FIOC 126 is located downstream from the FIOM/FIMOS 124in the feedback instance architecture 100. The illustrated FIOC 126includes the FIOC processing unit 322, the FIOC memory unit 324, and apolicy level tuner/adjuster (“PLTA”) module 1324. The PLTA module 1324can be stored in the FIOC memory unit 324 and can be executed by theFIOC processing unit 322 to cause the FIOC 126 to perform operationsdescribed herein.

The FIOC processing unit 322 can be or can include one or more CPUsconfigured with one or more processing cores. The FIOC processing unit322 can be or can include one or more GPUs configured to accelerateoperations performed by one or more CPUs, and/or to perform computationsto process data, and/or to execute computer-executable instructions ofone or more application programs, operating systems, and/or othersoftware that may or may not include instructions particular to graphicscomputations. In some embodiments, the FIOC processing unit 322 can beor can include one or more discrete GPUs. In some other embodiments, theFIOC processing unit 322 can be or can include CPU and GPU componentsthat are configured in accordance with a co-processing CPU/GPU computingmodel, wherein the sequential part of an application executes on the CPUand the computationally-intensive part is accelerated by the GPU. TheFIOC processing unit 322 can be or can include one or more FPGAs. TheFIOC processing unit 322 can be or can include one or more SoCcomponents along with one or more other components, including, forexample, the FIOC memory unit 324. In some embodiments, the FIOCprocessing unit 322 can be or can include one or more SNAPDRAGON SoCs,available from QUALCOMM of San Diego, Calif.; one or more TEGRA SoCs,available from NVIDIA of Santa Clara, Calif.; one or more HUMMINGBIRDSoCs, available from SAMSUNG of Seoul, South Korea; one or more OMAPSoCs, available from TEXAS INSTRUMENTS of Dallas, Tex.; one or morecustomized versions of any of the above SoCs; and/or one or moreproprietary SoCs. The FIOC processing unit 322 can be or can include oneor more hardware components architected in accordance with an ARMarchitecture, available for license from ARM HOLDINGS of Cambridge,United Kingdom. Alternatively, the FIOC processing unit 322 can be orcan include one or more hardware components architected in accordancewith an x86 architecture, such an architecture available from INTELCORPORATION of Mountain View, Calif., and others. Those skilled in theart will appreciate the implementation of the FIOC processing unit 322can utilize various computation architectures, and as such, the FIOCprocessing unit 322 should not be construed as being limited to anyparticular computation architecture or combination of computationarchitectures, including those explicitly disclosed herein.

The FIOC memory unit 324 can be or can include one or more hardwarecomponents that perform storage operations, including temporary and/orpermanent storage operations. In some embodiments, the FIOC memory unit324 can include volatile and/or non-volatile memory implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data disclosed herein. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid statememory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store data andwhich can be accessed by the FIOC processing unit 322.

The FIOC 126 can respond to policy participation level adjustmentrequests received from the FIMOS EIS module 1320 by executing the PLTAmodule 1324 to tune/adjust the policy participation level of thefeedback instance 128. The FIOC 126 can respond to the policyparticipation level of the feedback instance 128 by retrieving theassociated feedback model from the active feedback instance modelstorage 142 and can interact with actors in the feedback instance 128 toadjust the policy participation level for each actor.

The FIOC 126 can function as a supporting entity to the FIMOS 1302. Insome embodiments, the functionality of the FIOC 126 is combined with thefunctionality of the FIMOS 1302. In other embodiments, such as theillustrated embodiment, the FIOC 126 is implemented as an externalentity that is in communication with the FIMOS 1302.

Turning now to FIG. 14, a method 1400 for selecting feedback instancemodes will be described, according to an illustrative embodiment. Themethod 1400 begins and proceeds to operation 1402, where the FIOM 1300subscribes to and monitors one or more events that might impact a policyparticipation level of an active feedback instance, such as the feedbackinstance 128, or one or more actors thereof. From operation 1402, themethod 1400 proceeds to operation 1404, where the FIOM 1300 examines andforwards the event(s) to the FIMOS 1302.

From operation 1404, the method 1400 proceeds to operation 1406, wherethe FIMOS 1302 maps the event(s) to a set of policy participation levelpolicies. From operation 1406, the method 1400 proceeds to operation1408, where the FIMOS 1302 determines which participation level thefeedback instance 128 should be tuned to and duration of the newparticipation level. From operation 1408, the method 1400 proceeds tooperation 1410, where the FIMOS 1302 stores a feedback instance modeladjusted in accordance with the participation level determined atoperation 1408 in the active feedback instance model storage 142. Fromoperation 1410, the method 1400 proceeds to operation 1412, where theFIMOS 1302 directs the FIOC 126 to realize one or more feedbackinstances and/or adjust the feedback instance 128 based upon theadjusted feedback instance model.

From operation 1412, the method 1400 proceeds to operation 1414. Themethod 1400 ends at operation 1414.

One non-limiting example of the method 1400 will now be described. Inthis example, a first feedback instance can involve a networkcontroller, an orchestrator, an analytic module, and the PE 104. Thefirst feedback instance can identify compute and network trafficanomolies for a large international bank, in conjunction with strictpolicy rules that have to be followed in order to enforce performancesecurity requirements and guidelines during normal business hours. Therules may not be relaxed in after-hour time due to being internationalin nature. For purposes of this example, assume that a financial crisisin one of the nations has caused unusual traffic volumes to withdrawfunds. An anomaly is accordingly detected. Since the signature indicatesthat there is no security threat, and the anomaly is really due to thefinancial crisis, security policy does not need to play such a largerole in this scenario, and therefore the policy participation withrespect to the strictness of security enforcement is reduced in thisparticular case to allow more real-time allocation of VM resources tobest accommodate the actual situation.

FIG. 15 is a block diagram illustrating a computer system 1500configured to provide the functionality in accordance with variousembodiments of the concepts and technologies disclosed herein. In someimplementations, the policy requestor(s) 102, the PE 104, the policyrepository 106, the policy enforcement point(s) 108, the feedbackinstance model repository 110, the other repositories 112, the FIRC 114,the FICUR 116, the FIER 118, the FICIR 120, the FICOR 122, theFIOM/FIMOS 124, the FIOC 126, other components described herein, orcombinations thereof can be or can include one or more computers thatare configured the same as or like the architecture of the computersystem 1500. It should be understood, however, that modification to thearchitecture may be made to facilitate certain interactions amongelements described herein.

The computer system 1500 includes a processing unit 1502, a memory 1504,one or more user interface devices 1506, one or more input/output(“I/O”) devices 1508, and one or more network devices 1510, each ofwhich is operatively connected to a system bus 1512. The system bus 1512enables bi-directional communication between the processing unit 1502,the memory 1504, the user interface devices 1506, the I/O devices 1508,and the network devices 1510.

The processing unit 1502 may be a standard central processor thatperforms arithmetic and logical operations, a more specific purposeprogrammable logic controller (“PLC”), a programmable gate array, orother type of processor described herein or known to those skilled inthe art and suitable for controlling the operation of the computersystem 1500. Processing units are generally known, and therefore are notdescribed in further detail herein. The PE processing unit 300, the FIRCprocessing unit 310, the FIOC processing unit 322, the FICUR processingunit 506, the FIER processing unit 702, the FICIR processing unit 910,the FICOR processing unit 1110, the FIOM processing unit 1310, the FIMOSprocessing unit 1316, or a combination thereof can include one or moreof the processing units 1502.

The memory 1504 communicates with the processing unit 1502 via thesystem bus 1512. In some embodiments, the memory 1504 is operativelyconnected to a memory controller (not shown) that enables communicationwith the processing unit 1502 via the system bus 1512. The PE memoryunit 302, the FIRC memory unit 312, the FIOC memory unit 324, the FICURmemory unit 508, the FIER memory unit 704, the FICIR memory unit 912,the FICOR memory unit 1112, the FIOM memory unit 1312, the FIMOS memoryunit 1318, or a combination thereof can include one or more instances ofthe memory 1504.

The illustrated memory 1504 includes an operating system 1514 and one ormore program modules 1516. The operating system 1514 can include, but isnot limited to, members of the WINDOWS, WINDOWS CE, and/or WINDOWSMOBILE families of operating systems from MICROSOFT CORPORATION, theLINUX family of operating systems, the SYMBIAN family of operatingsystems from SYMBIAN LIMITED, the BREW family of operating systems fromQUALCOMM CORPORATION, the MAC OS, OS X, and/or iOS families of operatingsystems from APPLE CORPORATION, the FREEBSD family of operating systems,the SOLARIS family of operating systems from ORACLE CORPORATION, otheroperating systems, and the like.

The program modules 1516 may include various software and/or programmodules to perform the various operations described herein. The programmodules 1516 and/or other programs can be embodied in computer-readablemedia containing instructions that, when executed by the processing unit1502, perform various operations such as those described herein.According to embodiments, the program modules 1516 may be embodied inhardware, software, firmware, or any combination thereof. The programmodules 1516 can include the PE module 304, the EIS module 314, the PASmodule 316, the MES module 318, the MS module 326, the FES module 328,the VAS module 330, the FIER EIS module 706, the FIER EDS module 708,the FIRS module 712, the RAS module 714, the FICIR EIS module 914, theAPIMS module 916, the ERMS module 918, the FIAPIIS module 924, theFIERIS module 926, the FICOR EIS module 1114, the MES module 1116, theORS module 1118, the FIES module 1124, the COOS module 1126, or acombination thereof.

By way of example, and not limitation, computer-readable media mayinclude any available computer storage media or communication media thatcan be accessed by the computer system 1500. Communication mediaincludes computer-readable instructions, data structures, programmodules, or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics changed or set in a manner as to encode information inthe signal. By way of example, and not limitation, communication mediaincludes wired media such as a wired network or direct-wired connection,and wireless media such as acoustic, radio frequency (“RF”), infrared(“IR”) and other wireless media. Combinations of the any of the aboveshould also be included within the scope of computer-readable media.

Computer storage media includes volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules, or other data. Computer storage media includes, but isnot limited to, random access memory (“RAM”), read-only memory (“ROM”),erasable programmable ROM (“EPROM”), electrically erasable programmableROM (“EEPROM”), flash memory or other solid state memory technology,CD-ROM, digital versatile disks (“DVD”), or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by the computer system1500. In the claims, the phrase “computer storage medium” and variationsthereof does not include waves or signals per se and/or communicationmedia.

The user interface devices 1506 may include one or more devices withwhich a user accesses the computer system 1500. The user interfacedevices 1506 may include, but are not limited to, computers, servers,personal digital assistants (“PDAs”), cellular phones, smartphones, orany suitable computing devices. The I/O devices 1508 enable a user tointerface with the program modules 1516. In one embodiment, the I/Odevices 1508 are operatively connected to an I/O controller (not shown)that enables communication with the processing unit 1502 via the systembus 1512. The I/O devices 1508 may include one or more input devices,such as, but not limited to, a keyboard, a mouse, or an electronicstylus. Further, the I/O devices 1508 may include one or more outputdevices, such as, but not limited to, a display screen or a printer. Insome embodiments, the I/O devices 1508 can be used for manual controlsfor operations to exercise under certain situations.

The network devices 1510 enable the computer system 1500 to communicatewith other networks or remote systems via a network 1518 Examples of thenetwork devices 1510 include, but are not limited to, a modem, a RF orIR transceiver, a telephonic interface, a bridge, a router, or a networkcard. The network 1518 may include a wireless network such as, but notlimited to, a Wireless Local Area Network (“WLAN”), a Wireless Wide AreaNetwork (“WWAN”), a Wireless Personal Area Network (“WPAN”) such asprovided via BLUETOOTH technology, a Wireless Metropolitan Area Network(“WMAN”) such as a WiMAX network or metropolitan cellular network.Alternatively, the network 1518 may be a wired network such as, but notlimited to, a Wide Area Network (“WAN”), a wired Personal Area Network(“PAN”), or a wired Metropolitan Area Network (“MAN”). The network 1518may be any other network described herein.

Turning now to FIG. 16, details of a network 1600 are illustrated,according to an illustrative embodiment. The network 1600 includes acellular network 1602, a packet data network 1604, for example, theInternet, and a circuit-switched network 1606, for example, a publicswitched telephone network (“PSTN′). The cellular network 1602 includesvarious components such as, but not limited to, base transceiverstations (′BTSs”), node-B's or e-node-B's, base station controllers(“BSCs”), radio network controllers (“RNCs”), mobile switching centers(“MSCs”), mobile management entities (“MMEs”), short message servicecenters (“SMSCs”), multimedia messaging service centers (“MMSCs”), homelocation registers (“HLRs”), home subscriber servers (“HSSs”), visitorlocation registers (“VLRs”), charging platforms, billing platforms,voicemail platforms, GPRS core network components, location servicenodes, an IP multimedia subsystem (“IMS”), and the like. The cellularnetwork 1602 also includes radios and nodes for receiving andtransmitting voice, data, and combinations thereof to and from radiotransceivers, networks, the packet data network 1604, and thecircuit-switched network 1606.

A mobile communications device 1608, such as, for example, a cellulartelephone, a user equipment, a mobile terminal, a PDA, a laptopcomputer, a handheld computer, and combinations thereof, can beoperatively connected to the cellular network 1602. The cellular network1602 can be configured as a 2G global system for mobile communications(“GSM”) network and can provide data communications via general packetradio service (“GPRS”) and/or enhanced data rates for GSM evolution(“EDGE”). Additionally, or alternatively, the cellular network 1602 canbe configured as a 3G universal mobile telecommunications system(“UMTS”) network and can provide data communications via the high-speedpacket access (“HSPA”) protocol family, for example, high-speed downlinkpacket access (“HSDPA”), enhanced uplink (“EUL”) also referred to ashigh-speed uplink packet access (“HSUPA”), and HSPA+. The cellularnetwork 1602 also is compatible with 4G mobile communications standardssuch as long-term evolution (“LTE”), or the like, as well as evolved andfuture mobile standards.

The packet data network 1604 includes various devices, for example,servers, computers, databases, and other devices in communication withone another, as is generally known. The packet data network 1604 can beor can include the network 1518. The packet data network 1604 devicesare accessible via one or more network links. The servers often storevarious files that are provided to a requesting device such as, forexample, a computer, a terminal, a smartphone, or the like. Typically,the requesting device includes software (a “browser”) for executing aweb page in a format readable by the browser or other software. Otherfiles and/or data may be accessible via “links” in the retrieved files,as is generally known. In some embodiments, the packet data network 1604includes or is in communication with the Internet. The circuit-switchednetwork 1606 includes various hardware and software for providingcircuit-switched communications. The circuit-switched network 1606 mayinclude, or may be, what is often referred to as a plain old telephonesystem (“POTS”). The functionality of a circuit-switched network 1606 orother circuit-switched network are generally known and will not bedescribed herein in detail.

The illustrated cellular network 1602 is shown in communication with thepacket data network 1604 and a circuit-switched network 1606, though itshould be appreciated that this is not necessarily the case. One or moreInternet-capable devices 1610, for example, a PC, a laptop, a portabledevice, or another suitable device, can communicate with one or morecellular networks 1602, and devices connected thereto, through thepacket data network 1604. It also should be appreciated that theInternet-capable device 1610 can communicate with the packet datanetwork 1604 through the circuit-switched network 1606, the cellularnetwork 1602, and/or via other networks (not illustrated).

As illustrated, a communications device 1612, for example, a telephone,facsimile machine, modem, computer, or the like, can be in communicationwith the circuit-switched network 1606, and therethrough to the packetdata network 1604 and/or the cellular network 1602. It should beappreciated that the communications device 1612 can be anInternet-capable device, and can be substantially similar to theInternet-capable device 1610. In the specification, the network is usedto refer broadly to any combination of the networks 1602, 1604, 1606shown in FIG. 16 and/or the network 1518. It should be appreciated thatsubstantially all of the functionality described with reference to thenetwork 1518 can be performed by the cellular network 1602, the packetdata network 1604, and/or the circuit-switched network 1606, alone or incombination with other networks, network elements, and the like.

Based on the foregoing, it should be appreciated that concepts andtechnologies directed to the escalation of feedback instances have beendisclosed herein. Although the subject matter presented herein has beendescribed in language specific to computer structural features,methodological and transformative acts, specific computing machinery,and computer-readable media, it is to be understood that the conceptsand technologies disclosed herein are not necessarily limited to thespecific features, acts, or media described herein. Rather, the specificfeatures, acts and mediums are disclosed as example forms ofimplementing the concepts and technologies disclosed herein.

The subject matter described above is provided by way of illustrationonly and should not be construed as limiting. Various modifications andchanges may be made to the subject matter described herein withoutfollowing the example embodiments and applications illustrated anddescribed, and without departing from the true spirit and scope of theembodiments of the concepts and technologies disclosed herein.

The invention claimed is:
 1. A method comprising: receiving, by afeedback instance escalation resolver (“FIER”) system comprising aprocessor, a feedback instance escalation request, wherein the feedbackinstance escalation request notifies the FIER system of an issueassociated with a feedback instance; in response to the feedbackinstance escalation request, examining, by the FIER system, the issueassociated with the feedback instance to identify an escalated objectivefor an escalated feedback instance to address the issue; creating, bythe FIER system, a definition for the escalated feedback instance,wherein the definition comprises the escalated objective, a target, andan input to address the issue; mapping, by the FIER system, thedefinition for the escalated feedback instance to an existing feedbackinstance model; and generating, by the FIER system, a feedback instanceescalation realization request directed to a feedback instanceorchestrator and controller (“FIOC”) system, wherein the feedbackinstance escalation realization request instructs the FIOC system torealize the escalated feedback instance based upon the existing feedbackinstance model to address the issue associated with the feedbackinstance.
 2. The method of claim 1, wherein receiving, by the FIERsystem, the feedback instance escalation request comprises receiving, bythe FIER system, the feedback instance escalation request from a policyengine.
 3. The method of claim 1, wherein mapping, by the FIER system,the definition for the escalated feedback instance to the existingfeedback instance model comprises mapping, by the FIER system, thedefinition for the escalated feedback instance to an active feedbackinstance model.
 4. The method of claim 3, wherein mapping, by the FIERsystem, the definition for the escalated feedback instance to the activefeedback instance model comprises utilizing a table of available activefeedback instance models to map the definition for the escalatedfeedback instance to the active feedback instance model.
 5. The methodof claim 3, wherein mapping, by the FIER system, the definition for theescalated feedback instance to the existing feedback instance modelfurther comprises mapping, by the FIER system, the definition for theescalated feedback instance to a further active feedback instance model;and wherein the method further comprises: calculating a distance scorefor the active feedback instance model from the definition for theescalated feedback instance; calculating a further distance score forthe further active feedback instance model from the definition for theescalated feedback instance; and selecting either the distance score orthe further distance score based upon a closest match to the definitionfor the escalated feedback instance.
 6. The method of claim 1, whereinmapping, by the FIER system, the definition for the escalated feedbackinstance to the existing feedback instance model comprises mapping, bythe FIER system, the definition for the escalated feedback instance to anon-active feedback instance model.
 7. The method of claim 6, whereinmapping, by the FIER system, the definition for the escalated feedbackinstance to the non-active feedback instance model comprises utilizing atable of available non-active feedback instance models to map thedefinition for the escalated feedback instance to the non-activefeedback instance model.
 8. The method of claim 6, wherein mapping, bythe FIER system, the definition for the escalated feedback instance tothe existing feedback instance model further comprises mapping thedefinition for the escalated feedback instance to a further non-activefeedback instance model; and wherein the method further comprises:calculating a distance score for the non-active feedback instance modelfrom the definition for the escalated feedback instance; calculating afurther distance score for the further non-active feedback instancemodel from the definition for the escalated feedback instance; andselecting either the distance score or the further distance score basedupon a closest match to the definition for the escalated feedbackinstance.
 9. A computer-readable storage medium comprisingcomputer-executable instructions that, when executed by a processor of afeedback instance escalation resolver (“FIER”) system, cause theprocessor to perform operations comprising: receiving a feedbackinstance escalation request that notifies the FIER system of an issueassociated with a feedback instance; in response to the feedbackinstance escalation request, examining the issue associated with thefeedback instance to identify an escalated objective for an escalatedfeedback instance to address the issue; creating a definition for theescalated feedback instance, wherein the definition comprises theescalated objective, a target, and an input to address the issue;mapping the definition for the escalated feedback instance to anexisting feedback instance model; and generating a feedback instanceescalation realization request directed to a feedback instanceorchestrator and controller (“FIOC”) system, wherein the feedbackinstance escalation realization request instructs the FIOC system torealize the escalated feedback instance based upon the existing feedbackinstance model to address the issue associated with the feedbackinstance.
 10. The computer-readable storage medium of claim 9, whereinmapping the definition for the escalated feedback instance to theexisting feedback instance model comprises mapping the definition forthe escalated feedback instance to an active feedback instance model.11. The computer-readable storage medium of claim 10, wherein mappingthe definition for the escalated feedback instance to the activefeedback instance model comprises utilizing a table of available activefeedback instance models to map the definition for the escalatedfeedback instance to the active feedback instance model.
 12. Thecomputer-readable storage medium of claim 10, wherein mapping thedefinition for the escalated feedback instance to the existing feedbackinstance model further comprises mapping the definition for theescalated feedback instance to a further active feedback instance model;and wherein the operations further comprise: calculating a distancescore for the active feedback instance model from the definition for theescalated feedback instance; calculating a further distance score forthe further active feedback instance model from the definition for theescalated feedback instance; and selecting either the distance score orthe further distance score based upon a closest match to the definitionfor the escalated feedback instance.
 13. The computer-readable storagemedium of claim 9, wherein mapping the definition for the escalatedfeedback instance to the existing feedback instance model comprisesmapping the definition for the escalated feedback instance to anon-active feedback instance model.
 14. The computer-readable storagemedium of claim 13, wherein mapping the definition for the escalatedfeedback instance to the non-active feedback instance model comprisesutilizing a table of available non-active feedback instance models tomap the definition for the escalated feedback instance to the non-activefeedback instance model.
 15. The computer-readable storage medium ofclaim 13, wherein mapping the definition for the escalated feedbackinstance to the existing feedback instance model further comprisesmapping the definition for the escalated feedback instance to a furthernon-active feedback instance model; and wherein the operations furthercomprise: calculating a distance score for the non-active feedbackinstance model from the definition for the escalated feedback instance;calculating a further distance score for the further non-active feedbackinstance model from the definition for the escalated feedback instance;and selecting either the distance score or the further distance scorebased upon a closest match to the definition for the escalated feedbackinstance.
 16. A system comprising; a processor; and memory comprisingcomputer-executable instructions that, when executed by the processor,cause the processor to perform operations comprising receiving afeedback instance escalation request that notifies the system of anissue associated with a feedback instance, in response to the feedbackinstance escalation request, examining the issue associated with thefeedback instance to identify an escalated objective for an escalatedfeedback instance to address the issue, creating a definition for theescalated feedback instance, wherein the definition comprises theescalated objective, a target, and an input to address the issue,mapping the definition for the escalated feedback instance to anexisting feedback instance model, and generating a feedback instanceescalation realization request directed to a feedback instanceorchestrator and controller (“FIOC”) system, wherein the feedbackinstance escalation realization request instructs the FIOC system torealize the escalated feedback instance based upon the existing feedbackinstance model to address the issue associated with the feedbackinstance.
 17. The system of claim 16, wherein mapping the definition forthe escalated feedback instance to the existing feedback instance modelcomprises mapping the definition for the escalated feedback instance toan active feedback instance model or a non-active feedback instancemodel.
 18. The system of claim 17, wherein mapping the definition forthe escalated feedback instance to the existing feedback instance modelfurther comprises mapping the definition for the escalated feedbackinstance to a further active feedback instance model; and wherein theoperations further comprise: calculating a distance score for the activefeedback instance model from the definition for the escalated feedbackinstance; calculating a further distance score for the further activefeedback instance model from the definition for the escalated feedbackinstance; and selecting either the distance score or the furtherdistance score based upon a closest match to the definition for theescalated feedback instance.
 19. The system of claim 17, wherein mappingthe definition for the escalated feedback instance to the existingfeedback instance model further comprises mapping the definition for theescalated feedback instance to a further non-active feedback instancemodel; and wherein the operations further comprise: calculating adistance score for the non-active feedback instance model from thedefinition for the escalated feedback instance; calculating a furtherdistance score for the further non-active feedback instance model fromthe definition for the escalated feedback instance; and selecting eitherthe distance score or the further distance score based upon a closestmatch to the definition for the escalated feedback instance.